集中更新所有未更新的

TensorFlow_eaxmple/IMDB/IMDB.py

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+import urllib.request
+import os
+import tarfile
+
+#下载数据集
+url="http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
+filepath="data/aclImdb_v1.tar.gz"
+if not os.path.isfile(filepath):
+    result=urllib.request.urlretrieve(url,filepath)
+    print('downloaded:',result)
+# 解压
+if not os.path.exists("data/aclImdb"):
+    tfile = tarfile.open("data/aclImdb_v1.tar.gz", 'r:gz')
+    result=tfile.extractall('data/')

TensorFlow_eaxmple/IMDB/model.py

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+
+
+
+
+
+

TensorFlow_eaxmple/IMDB/traning.py

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+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation
+from keras.layers.embeddings import Embedding
+from keras.layers.recurrent import SimpleRNN
+import matplotlib.pyplot as plt
+from keras.datasets import imdb
+from keras.preprocessing import sequence
+from keras.preprocessing.text import Tokenizer
+import re
+import os
+
+import urllib.request
+import os
+import tarfile
+
+# 下载数据集
+url = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
+filepath = "data/aclImdb_v1.tar.gz"
+if not os.path.isfile(filepath):
+    result = urllib.request.urlretrieve(url, filepath)
+    print('downloaded:', result)
+# 解压
+if not os.path.exists("data/aclImdb"):
+    tfile = tarfile.open("data/aclImdb_v1.tar.gz", 'r:gz')
+    result = tfile.extractall('data/')
+
+re_tag = re.compile(r'<[^>]+>')
+
+
+def rm_tags(text):
+    return re_tag.sub('', text)
+
+
+def read_files(filetype):
+    path = "data/aclImdb/"
+    file_list = []
+
+    positive_path = path + filetype + "/pos/"
+    for f in os.listdir(positive_path):
+        file_list += [positive_path + f]
+
+    negative_path = path + filetype + "/neg/"
+    for f in os.listdir(negative_path):
+        file_list += [negative_path + f]
+
+    print('read', filetype, 'files:', len(file_list))
+
+    all_labels = ([1] * 12500 + [0] * 12500)
+
+    all_texts = []
+
+    for fi in file_list:
+        with open(fi, encoding='utf8') as file_input:
+            all_texts += [rm_tags(" ".join(file_input.readlines()))]
+
+    return all_labels, all_texts
+
+
+# 读文件
+y_train, train_text = read_files("train")
+y_test, test_text = read_files("test")
+
+# 建立单词和数字映射的字典
+token = Tokenizer(num_words=3800)
+token.fit_on_texts(train_text)
+
+# 将影评的单词映射到数字
+x_train_seq = token.texts_to_sequences(train_text)
+x_test_seq = token.texts_to_sequences(test_text)
+
+# 让所有影评保持在380个数字
+x_train = sequence.pad_sequences(x_train_seq, maxlen=380)
+x_test = sequence.pad_sequences(x_test_seq, maxlen=380)
+
+model = Sequential()
+
+model.add(Embedding(output_dim=32,
+                    input_dim=3800,
+                    input_length=380))
+model.add(Dropout(0.35))
+
+# 加了一个简单的RNN层
+model.add(SimpleRNN(units=16))
+
+model.add(Dense(units=256, activation='relu'))
+model.add(Dropout(0.35))
+
+model.add(Dense(units=1, activation='sigmoid'))
+
+model.summary()
+'''
+Model: "sequential_1"
+_________________________________________________________________
+Layer (type)                 Output Shape              Param #   
+=================================================================
+embedding_1 (Embedding)      (None, 380, 32)           121600    
+_________________________________________________________________
+dropout_1 (Dropout)          (None, 380, 32)           0         
+_________________________________________________________________
+simple_rnn_1 (SimpleRNN)     (None, 16)                784       
+_________________________________________________________________
+dense_1 (Dense)              (None, 256)               4352      
+_________________________________________________________________
+dropout_2 (Dropout)          (None, 256)               0         
+_________________________________________________________________
+dense_2 (Dense)              (None, 1)                 257       
+=================================================================
+Total params: 126,993
+Trainable params: 126,993
+Non-trainable params: 0
+_________________________________________________________________
+'''
+
+model.compile(loss='binary_crossentropy',
+              optimizer='adam',
+              metrics=['accuracy'])
+
+train_history = model.fit(x_train, y_train, batch_size=100,
+                          epochs=10, verbose=2,
+                          validation_split=0.2)
+
+
+def show_train_history(train_history, train, validation):
+    plt.plot(train_history.history[train])
+    plt.plot(train_history.history[validation])
+    plt.title('Train History')
+    plt.ylabel(train)
+    plt.xlabel('Epoch')
+    plt.legend(['train', 'validation'], loc='upper left')
+    plt.savefig('1.png')
+    plt.show()
+
+
+show_train_history(train_history, 'accuracy', 'val_accuracy')
+show_train_history(train_history, 'loss', 'val_loss')
+
+scores = model.evaluate(x_test, y_test, verbose=1)
+print(scores[1])  #0.8435199856758118
+
+probility = model.predict(x_test)
+print(probility[:10])
+'''
+[[0.99944454]
+ [0.9945456 ]
+ [0.9609897 ]
+ [0.9960341 ]
+ [0.94589317]
+ [0.99465704]
+ [0.9959715 ]
+ [0.77324533]
+ [0.99609864]
+ [0.99585694]]
+'''
+
+predict = model.predict_classes(x_test)
+print(predict[:10])
+'''
+[[1]
+ [1]
+ [1]
+ [1]
+ [1]
+ [1]
+ [1]
+ [1]
+ [1]
+ [1]]
+'''
+
+SentimentDict = {1: '正面的', 0: '负面的'}
+
+
+def display_test_Sentiment(i):
+    print(test_text[i])
+    print('label:', SentimentDict[y_test[i]],
+          '预测结果:', SentimentDict[predict[i][0]])
+
+    '''
+    label: 正面的 预测结果: 正面的
+    负面的
+    '''
+
+
+display_test_Sentiment(2)
+
+
+def predict_review(input_text):
+    # 影评转换为数字列表
+    input_seq = token.texts_to_sequences([input_text])
+    # 截断数字列表使得所有输入长度为380
+    pad_input_seq = sequence.pad_sequences(input_seq, maxlen=380)
+    # 预测分类结果
+    predict_result = model.predict_classes(pad_input_seq)
+    # 输出结果
+    print(SentimentDict[predict_result[0][0]])
+
+
+predict_review('''
+As a fan of the original Disney film (Personally I feel it’s their masterpiece) I was taken aback to the fact that a new version was in the making. Still excited I had high hopes for the film. Most of was shattered in the first 10 minutes. Campy acting with badly performed singing starts off a long journey holding hands with some of the worst CGI Hollywood have managed to but to screen in ages.
+A film that is over 50% GCI, should focus on making that part believable, unfortunately for this film, it’s far from that. It looks like the original film was ripped apart frame by frame and the beautiful hand-painted drawings have been replaced with digital caricatures. Besides CGI that is bad, it’s mostly creepy. As the little teacup boy will give me nightmares for several nights to come. Emma Watson plays the same character as she always does, with very little acting effort and very little conviction as Belle. Although I can see why she was cast in the film based on merits, she is far from the right choice for the role. Dan Stevens does alright under as some motion captured dead-eyed Beast, but his performance feels flat as well. Luke Evans makes for a great pompous Gaston, but a character that has little depth doesn’t really make for a great viewing experience. Josh Gad is a great comic relief just like the original movie’s LeFou. Other than that, none of the cast stands out enough for me to remember them. Human or CHI creature. I was just bored through out the whole experience. And for a project costing $160 000 000, I can see why the PR department is pushing it so hard because they really need to get some cash back on this pile of wet stinky CGI-fur!
+All and all, I might be bias from really loving Disney’s first adaptation. That for me marks the high-point of all their work, perfectly combining the skills of their animators along with some CGI in a majestic blend. This film however is more like the bucket you wash off your paintbrush in, it has all the same colors, but muddled with water and to thin to make a captivating story from. The film is quite frankly not worth your time, you would be better off watching the original one more time.
+''')
+
+

TensorFlow_eaxmple/Model_train_test/2012轴承数据集预测挑战/datadeal/datadeal.py

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+import tensorflow as tf
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import os
+import scipy.io as io
+
+# configuration=pd.read_csv('E:\data\PHM 2012轴承挑战数据集\phm-ieee-2012-data-challenge-dataset-master\Learning_set\Bearing1_1\\acc_00001.csv',dtype=np.float32,header=None)
+# print(configuration.shape)
+# x=np.arange(2560)
+# y=configuration.iloc[:,4]
+# # y=configuration.iloc[:,5]
+# plt.plot(x,y)
+# plt.show()
+
+
+L = []
+for root, dir, filename in os.walk(
+        'E:\data\PHM 2012轴承挑战数据集\phm-ieee-2012-data-challenge-dataset-master\Learning_set\Bearing1_1'):
+    for file in filename:
+        if os.path.splitext(file)[1] == '.csv':
+            L.append(
+                'E:\data\PHM 2012轴承挑战数据集\phm-ieee-2012-data-challenge-dataset-master\Learning_set\Bearing1_1' + '/' + file)
+
+z = 0
+
+for filename in L:
+    data_single = pd.read_csv(filename, dtype=np.float32, header=None)
+    data_single = data_single.iloc[:, 4]
+    if z == 0:
+        # data_all=data_single
+        HI_data = data_single
+    else:
+        # data_all=np.hstack([data_all,data_single])
+        HI_data = np.vstack([HI_data, data_single])
+    z += 1
+
+print(z)
+# print(data_all.shape)
+print(HI_data.shape)
+io.savemat("./HI_data.mat", {'HI_data': HI_data})
+# np.save("../data/HI_DATA/HI_data.npy",HI_data)
+# np.save("data_y.npy",data_all)
+#
+# data_x=np.arange(7175680)
+# np.save("data_x.npy",data_x)

TensorFlow_eaxmple/Model_train_test/2012轴承数据集预测挑战/other_LSTM_attention/train.py

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+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import tensorflow.keras.backend as K
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.callbacks import *
+from tensorflow.keras.initializers import *
+from tensorflow.keras.layers import *
+from tensorflow.keras.models import *
+from tensorflow.python.keras.layers import Layer
+
+file_path = "./shortVHI.csv"
+
+df = pd.read_csv(file_path)
+
+plt.figure()
+plt.plot(range(df.shape[0]), (df['NDX']))
+plt.xlabel('time', fontsize=18)
+plt.ylabel('NDX', fontsize=18)
+plt.show()
+
+BATCH_SIZE = 128
+EPOCHS = 3
+SEQ_LEN = 10
+FUTURE_PERIOD_PREDICT = 1
+RATIO_TO_PREDICT = "NDX"
+targ_cols = ("NDX",)
+
+
+# 归一化，划分feature target
+def preprocess_data(dat, col_names):
+    scale = MinMaxScaler().fit(dat)
+    proc_dat = scale.transform(dat)
+
+    mask = np.ones(proc_dat.shape[1], dtype=bool)
+    dat_cols = list(dat.columns)
+    for col_name in col_names:
+        mask[dat_cols.index(col_name)] = False
+
+    feats = proc_dat[:, mask]
+    targs = proc_dat[:, ~mask]
+
+    return feats, targs
+
+
+# 把数据切分成80%训练集、20%测试集
+def split_data(data, percent_train=0.70):
+    num_rows = len(data)
+    train_data, test_data = [], []
+    for idx, row in enumerate(data):
+        if idx < num_rows * percent_train:
+            train_data.append(row)
+        else:
+            test_data.append(row)
+    return np.array(train_data), np.array(test_data)
+
+
+data_X, data_y = preprocess_data(df, targ_cols)
+print(data_X.shape, data_y.shape)
+all_data = np.concatenate([data_X, data_y], axis=1)
+print(all_data.shape)
+
+train_, test_ = split_data(all_data)
+print("train shape {0}".format(train_.shape))
+print("test shape {0}".format(test_.shape))
+
+
+def timestamp_data(data):
+    X = []
+    Y = []
+    for i in range(SEQ_LEN, len(data) - FUTURE_PERIOD_PREDICT + 1):
+        X.append(data[i - SEQ_LEN:i, :data_X.shape[1]])  # 含左不含右
+        Y.append(data[i + (FUTURE_PERIOD_PREDICT - 1), data_X.shape[1]])
+    return np.array(X), np.array(Y)
+
+
+X_train, y_train = timestamp_data(train_)
+print('X_train', X_train.shape, 'y_train', y_train.shape)
+X_test, y_test = timestamp_data(test_)
+
+X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], data_X.shape[1]))
+X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], data_X.shape[1]))
+
+print("train shape {0}".format(X_train.shape))
+# print("valid shape {0}".format(X_valid.shape))
+print("test shape {0}".format(X_test.shape))
+
+
+class Attention(Layer):
+    def __init__(self, step_dim,
+                 W_regularizer=None, b_regularizer=None,
+                 W_constraint=None, b_constraint=None,
+                 bias=True, **kwargs):
+        self.supports_masking = True
+        self.init = tf.keras.initializers.get('glorot_uniform')
+        # W_regularizer: 权重上的正则化
+        # b_regularizer: 偏置项的正则化
+        self.W_regularizer = tf.keras.regularizers.get(W_regularizer)
+        self.b_regularizer = tf.keras.regularizers.get(b_regularizer)
+        # W_constraint: 权重上的约束项
+        # b_constraint: 偏置上的约束项
+        self.W_constraint = tf.keras.constraints.get(W_constraint)
+        self.b_constraint = tf.keras.constraints.get(b_constraint)
+
+        self.bias = bias
+        self.step_dim = step_dim
+        self.features_dim = 0
+        super(Attention, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        assert len(input_shape) == 3
+
+        self.W = self.add_weight(shape=(input_shape[-1],),
+                                 initializer=self.init,
+                                 name='{}_W'.format(self.name),
+                                 regularizer=self.W_regularizer,
+                                 constraint=self.W_constraint)
+        self.features_dim = input_shape[-1]
+
+        if self.bias:
+            self.b = self.add_weight(shape=(input_shape[1],),
+                                     initializer='zero',
+                                     name='{}_b'.format(self.name),
+                                     regularizer=self.b_regularizer,
+                                     constraint=self.b_constraint)
+        else:
+            self.b = None
+
+        self.built = True
+
+    def compute_mask(self, input, input_mask=None):
+        return None
+
+    def call(self, x, mask=None):
+        features_dim = self.features_dim
+        step_dim = self.step_dim
+
+        eij = K.reshape(K.dot(K.reshape(x, (-1, features_dim)),
+                              K.reshape(self.W, (features_dim, 1))), (-1, step_dim))
+
+        if self.bias:
+            eij += self.b
+
+        eij = K.tanh(eij)
+
+        a = K.exp(eij)
+
+        '''
+        keras.backend.cast(x, dtype): 将张量转换到不同的 dtype 并返回
+        '''
+        if mask is not None:
+            a *= K.cast(mask, K.floatx())
+
+        '''
+        keras.backend.epsilon(): 返回浮点数
+        '''
+        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
+
+        a = K.expand_dims(a)
+        weighted_input = x * a
+
+        return K.sum(weighted_input, axis=1)
+
+    def compute_output_shape(self, input_shape):
+        return input_shape[0], self.features_dim
+
+
+inp = Input(shape=(SEQ_LEN, data_X.shape[1]))
+x = GRU(256, return_sequences=True)(inp)
+x = Dropout(0.2)(x)
+x = BatchNormalization()(x)
+x = Attention(SEQ_LEN)(x)
+x = Dense(32, activation="relu")(x)
+x = Dropout(0.2)(x)
+x = Dense(1, activation="relu")(x)
+model_lstm_attention = tf.keras.Model(inputs=inp, outputs=x)
+model_lstm_attention.compile(loss='mean_squared_error', optimizer='adam')
+model_lstm_attention.summary()
+
+earlyStopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='auto')
+
+tf.keras.utils.plot_model(model_lstm_attention,
+                          to_file="model_lstm_attention.png",
+                          show_shapes=True)
+
+model_lstm_attention.fit(X_train, y_train,
+                         batch_size=BATCH_SIZE,
+                         epochs=EPOCHS,
+                         validation_data=(X_test, y_test))
+
+# model_lstm_attention.save('lstm+gru.h5')
+
+predicted_LSTM_Att = model_lstm_attention.predict(X_test)
+
+# 绘图
+plt.plot(list(range(X_test.shape[0])), y_test, label='True')
+plt.plot(list(predicted_LSTM_Att), label='Predicted Test')
+plt.title('ffmpeg-power')
+plt.xlabel('DateTime')
+plt.ylabel('power')
+plt.legend()
+plt.savefig(
+    "/Users/Arithmetic/pythonProject/lstm+attention/result_pics/lstm+attention_nasdaq_epoch{0}.png".format(EPOCHS))
+plt.show()

TensorFlow_eaxmple/Model_train_test/2012轴承数据集预测挑战/other_LSTM_attention/train1.py

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+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import tensorflow.keras.backend as K
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.callbacks import *
+from tensorflow.keras.initializers import *
+from tensorflow.keras.layers import *
+from tensorflow.keras.models import *
+from tensorflow.python.keras.layers import Layer
+
+file_path = "./shortVHI.csv"
+
+df = pd.read_csv(file_path)
+
+plt.figure()
+plt.plot(range(df.shape[0]), (df['NDX']))
+plt.xlabel('time', fontsize=18)
+plt.ylabel('NDX', fontsize=18)
+plt.show()
+
+BATCH_SIZE = 128
+EPOCHS = 3
+SEQ_LEN = 10
+FUTURE_PERIOD_PREDICT = 1
+RATIO_TO_PREDICT = "NDX"
+targ_cols = ("NDX",)
+
+
+# 归一化，划分feature target
+def preprocess_data(dat, col_names):
+    scale = MinMaxScaler().fit(dat)
+    proc_dat = scale.transform(dat)
+
+    mask = np.ones(proc_dat.shape[1], dtype=bool)
+    dat_cols = list(dat.columns)
+    for col_name in col_names:
+        mask[dat_cols.index(col_name)] = False
+
+    feats = proc_dat[:, mask]
+    targs = proc_dat[:, ~mask]
+
+    return feats, targs
+
+
+# 把数据切分成80%训练集、20%测试集
+def split_data(data, percent_train=0.70):
+    num_rows = len(data)
+    train_data, test_data = [], []
+    for idx, row in enumerate(data):
+        if idx < num_rows * percent_train:
+            train_data.append(row)
+        else:
+            test_data.append(row)
+    return np.array(train_data), np.array(test_data)
+
+
+data_X, data_y = preprocess_data(df, targ_cols)
+print(data_X.shape, data_y.shape)
+all_data = np.concatenate([data_X, data_y], axis=1)
+print(all_data.shape)
+
+train_, test_ = split_data(all_data)
+print("train shape {0}".format(train_.shape))
+print("test shape {0}".format(test_.shape))
+
+
+def timestamp_data(data):
+    X = []
+    Y = []
+    for i in range(SEQ_LEN, len(data) - FUTURE_PERIOD_PREDICT + 1):
+        X.append(data[i - SEQ_LEN:i, :data_X.shape[1]])  # 含左不含右
+        Y.append(data[i + (FUTURE_PERIOD_PREDICT - 1), data_X.shape[1]])
+    return np.array(X), np.array(Y)
+
+
+X_train, y_train = timestamp_data(train_)
+print('X_train', X_train.shape, 'y_train', y_train.shape)
+X_test, y_test = timestamp_data(test_)
+
+X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], data_X.shape[1]))
+X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], data_X.shape[1]))
+
+print("train shape {0}".format(X_train.shape))
+# print("valid shape {0}".format(X_valid.shape))
+print("test shape {0}".format(X_test.shape))
+
+
+class Attention(Layer):
+    def __init__(self, step_dim,
+                 W_regularizer=None, b_regularizer=None,
+                 W_constraint=None, b_constraint=None,
+                 bias=True, **kwargs):
+        self.supports_masking = True
+        self.init = tf.keras.initializers.get('glorot_uniform')
+        # W_regularizer: 权重上的正则化
+        # b_regularizer: 偏置项的正则化
+        self.W_regularizer = tf.keras.regularizers.get(W_regularizer)
+        self.b_regularizer = tf.keras.regularizers.get(b_regularizer)
+        # W_constraint: 权重上的约束项
+        # b_constraint: 偏置上的约束项
+        self.W_constraint = tf.keras.constraints.get(W_constraint)
+        self.b_constraint = tf.keras.constraints.get(b_constraint)
+
+        self.bias = bias
+        self.step_dim = step_dim
+        self.features_dim = 0
+        super(Attention, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        assert len(input_shape) == 3
+
+        self.W = self.add_weight(shape=(input_shape[-1],),
+                                 initializer=self.init,
+                                 name='{}_W'.format(self.name),
+                                 regularizer=self.W_regularizer,
+                                 constraint=self.W_constraint)
+        self.features_dim = input_shape[-1]
+
+        if self.bias:
+            self.b = self.add_weight(shape=(input_shape[1],),
+                                     initializer='zero',
+                                     name='{}_b'.format(self.name),
+                                     regularizer=self.b_regularizer,
+                                     constraint=self.b_constraint)
+        else:
+            self.b = None
+
+        self.built = True
+
+    def compute_mask(self, input, input_mask=None):
+        return None
+
+    def call(self, x, mask=None):
+        features_dim = self.features_dim
+        step_dim = self.step_dim
+
+        eij = K.reshape(K.dot(K.reshape(x, (-1, features_dim)),
+                              K.reshape(self.W, (features_dim, 1))), (-1, step_dim))
+
+        if self.bias:
+            eij += self.b
+
+        eij = K.tanh(eij)
+
+        a = K.exp(eij)
+
+        '''
+        keras.backend.cast(x, dtype): 将张量转换到不同的 dtype 并返回
+        '''
+        if mask is not None:
+            a *= K.cast(mask, K.floatx())
+
+        '''
+        keras.backend.epsilon(): 返回浮点数
+        '''
+        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
+
+        a = K.expand_dims(a)
+        weighted_input = x * a
+
+        return K.sum(weighted_input, axis=1)
+
+    def compute_output_shape(self, input_shape):
+        return input_shape[0], self.features_dim
+
+
+inp = Input(shape=(SEQ_LEN, data_X.shape[1]))
+x = GRU(256, return_sequences=True)(inp)
+x = Dropout(0.2)(x)
+x = BatchNormalization()(x)
+x = Attention(SEQ_LEN)(x)
+x = Dense(32, activation="relu")(x)
+x = Dropout(0.2)(x)
+x = Dense(1, activation="relu")(x)
+model_lstm_attention = tf.keras.Model(inputs=inp, outputs=x)
+model_lstm_attention.compile(loss='mean_squared_error', optimizer='adam')
+model_lstm_attention.summary()
+
+earlyStopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='auto')
+
+tf.keras.utils.plot_model(model_lstm_attention,
+                          to_file="model_lstm_attention.png",
+                          show_shapes=True)
+
+model_lstm_attention.fit(X_train, y_train,
+                         batch_size=BATCH_SIZE,
+                         epochs=EPOCHS,
+                         validation_data=(X_test, y_test))
+
+# model_lstm_attention.save('lstm+gru.h5')
+
+predicted_LSTM_Att = model_lstm_attention.predict(X_test)
+
+# 绘图
+plt.plot(list(range(X_test.shape[0])), y_test, label='True')
+plt.plot(list(predicted_LSTM_Att), label='Predicted Test')
+plt.title('ffmpeg-power')
+plt.xlabel('DateTime')
+plt.ylabel('power')
+plt.legend()
+plt.savefig(
+    "/Users/Arithmetic/pythonProject/lstm+attention/result_pics/lstm+attention_nasdaq_epoch{0}.png".format(EPOCHS))
+plt.show()

TensorFlow_eaxmple/Model_train_test/condition_monitoring/data_deal/test.py

@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+'''
+@Author : dingjiawen
+@Date : 2022/7/22 14:44
+@Usage : 
+@Desc :
+'''
+
+import numpy as np;
+import matplotlib.pyplot as plt
+from matplotlib.collections import LineCollection
+from mpl_toolkits.mplot3d import Axes3D
+import waterfall_chart as waterfall
+
+
+def waterfall_plot(fig, ax, X, Y, Z, **kwargs):
+    '''    Make a waterfall plot
+    Input:
+    fig,ax : matplotlib figure and axes to populate
+    Z : n,m numpy array. Must be a 2d array even if only one line should be plotted
+    X,Y : n,m array
+    kwargs : kwargs are directly passed to the LineCollection object
+    '''
+    # Set normalization to the same values for all plots
+    norm = plt.Normalize(Z.min().min(), Z.max().max())
+    # Check sizes to loop always over the smallest dimension
+    n, m = Z.shape
+    if n > m:
+        X = X.T
+        Y = Y.T
+        Z = Z.T
+        m, n = n, m
+        for j in range(n):
+            # reshape the X,Z into pairs
+            points = np.array([X[j, :], Z[j, :]]).T.reshape(-1, 1, 2)
+        segments = np.concatenate([points[:-1], points[1:]], axis=1)
+        # The values used by the colormap are the input to the array parameter
+        lc = LineCollection(segments, cmap='plasma', norm=norm, array=(Z[j, 1:] + Z[j, :-1]) / 2, **kwargs)
+        line = ax.add_collection3d(lc, zs=(Y[j, 1:] + Y[j, :-1]) / 2, zdir='y')  # add line to axes
+        fig.colorbar(lc)  # add colorbar, as the normalization is the same for all
+        # it doesent matter which of the lc objects we use
+        ax.auto_scale_xyz(X, Y, Z)
+        # set axis limits
+        plt.plot(X,Y,Z)
+
+def execute(X,Y,Z):
+    waterfall.plot()
+
+
+
+if __name__ == '__main__':
+    # Generate data
+    x = np.linspace(-2, 2, 500)
+    y = np.linspace(-2, 2, 60)
+    X, Y = np.meshgrid(x, y)
+    Z = np.sin(X ** 2 + Y ** 2) - .2 * X
+    # Generate waterfall plot
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+    waterfall_plot(fig, ax, X, Y, Z, linewidth=1.5, alpha=0.5)
+    ax.set_xlabel('X');
+    ax.set_ylabel('Y');
+    ax.set_zlabel('Z')
+    fig.tight_layout()
+    plt.show()

TensorFlow_eaxmple/Model_train_test/condition_monitoring/data_deal/test2.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+
+# coding: utf-8
+
+'''
+@Author : dingjiawen
+@Date : 2022/7/22 15:08
+@Usage : 
+@Desc :
+'''
+
+#导入库
+import plotly.graph_objects as go
+import pandas as pd
+#数据集
+df = pd.read_csv(r'D:/netflix_titles.csv')
+#添加年和月并转换为正确的日期时间格式
+df["date_adding"] = pd.to_datetime(df['date_ added'])
+df['year_addition'] = df['date_ added'].dt.year
+df['month_ added'] = df['date_addition'].dt.month
+df.head(3)
+#准备数据
+d2 = df[df["type"] == "Movie"]
+col = "year_added"
+vc2 = d2[col].value_counts().reset_index().rename(columns = {col : "count", "index" : col})
+vc2['percent'] = vc2['count'].apply(lambda x : 100*x/sum(vc2['count']))
+vc2 = vc2.sort_values(col)
+#绘制瀑布图
+#现在我们将使用 Plotly trace go.Waterfall() 制作一个瀑布图。
+fig2 = go.Figure(go.Waterfall(
+    name = "Movie",
+    orientation = "v",
+    x = ["2008", "2009", "2010", "2011", "2012",
+         "2013", "2014", "2015", "2016", "2017",
+         "2018", "2019", "2020", "2021"],
+    textposition = "auto",
+    text = ["1", "2", "1", "13", "3", "6", "14", "48",
+            "204", "743", "1121", "1366", "1228", "84"],
+    y = [1, 2, -1, 13, -3, 6, 14, 48, 204, 743, 1121, 1366, -1228, -84],
+    connector = {"line":{"color":"#b20710"}},
+    increasing = {"marker":{"color":"#b20710"}},
+    decreasing = {"marker":{"color":"orange"}},
+))

TensorFlow_eaxmple/Model_train_test/condition_monitoring/lib/IOBase.py

@@ -0,0 +1,352 @@
+# @Time :2020/7/24 12:57
+# @Author : cqu.hhh | AlbertHu
+# @Site :CQU
+# @Desc :数据格式为，[...,时间，齿轮箱参数]
+'''
+---------  历史更新  ----------
+cqu.hhh
+    2020/07/28 对代码结构进行了重新设计
+
+-------------------------------
+'''
+import os
+import re
+import pandas as pd
+import pickle
+import json
+
+# from xml.dom.minidom import parse
+from tensorflow.keras.models import load_model
+
+'''
+    IO操作类
+'''
+
+
+class IOLib:
+    def __init__(self):
+        pass
+
+    def mkDirs(self, path):
+        """
+        创建多层目录
+        @param path:路径
+        """
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+    def findAllFilesPath(self, path):
+        """
+        寻找父目录包括子目录下所有文件的地址
+        @param path: 父目录地址
+        @return: 文件的地址集合
+        """
+        filePaths = []
+        files = os.listdir(path)
+        for file in files:
+            fi_d = os.path.join(path, file)
+            if os.path.isdir(fi_d):
+                files.extend(self.findAllFilesPath(fi_d))
+            else:
+                filePaths.append(fi_d)
+        return filePaths
+
+    def extractFile(self, filePath, params):
+        """
+        提取SCADA文件的时间数据与参数数据
+        @param filePath: SCADA文件路径
+        @param params: 模型训练与测试所需的参数。默认来自于'./conf/AE_Params.txt'
+        @return: 时间数据，参数数据
+        """
+        temp = pd.read_csv(filePath, encoding='gbk', parse_dates=[params[0]])
+        data = {}
+        time = temp[params[0]].values.astype(str)
+        for param in params[1:]:
+            data[param] = temp[param].values
+        data = pd.DataFrame(data)
+        data = data.values.astype('float32')
+        # print("========== 数据提取完毕,开始预测 ==========")
+        return time, data
+
+    # def extractXML(self, filePath, *params):
+    #     """
+    #     提取XML配置文件信息
+    #     @param filePath: xml文件路径
+    #     @param params: 节点名
+    #     @return: list(结果)
+    #     """
+    #     # from xml.dom.minidom import parse
+    #     titles = []
+    #     dom = parse(filePath)
+    #     properties = dom.getElementsByTagName(params[0])
+    #     for param in params[1:]:
+    #         properties = properties.item(0).getElementsByTagName(param)
+    #     for property in properties:
+    #         titles.append(property.getElementsByTagName('value')[0].childNodes[0].nodeValue)
+    #     return titles
+
+    def SplitData(self, DataFrameData):
+        alldata = []
+        sum = 0
+        data2Dict = DataFrameData
+        # 此处是否填补空值
+        # data2Dict = data2Dict.fillna(method='bfill')
+        data = data2Dict.dropna(axis=0)  # 删除空值行
+        namelist = data['farmid'].unique()  # 按不重复的方式排列
+        for i in range(len(namelist)):
+            data_i = data[data['farmid'].isin([namelist[i]])]
+            numlist = data_i['wtgid'].unique()
+            for j in range(len(numlist)):
+                data_i_j = data_i[data_i['wtgid'].isin([numlist[j]])]
+                sum = sum + 1
+                alldata.append(data_i_j)
+        return alldata, sum
+
+    def AddNullrow(self, PreDataframe, PostDataframe):
+        """
+        :param PreDataframe:去除空值前的数据
+        :param PostDataframe: 去除空值后的数据
+        :return:
+        """
+        pre = PreDataframe
+        post = PostDataframe
+        postfarmid = post['farmid'].tolist()
+        prefarmid = pre['farmid'].tolist()
+        postwtgid = post['wtgid'].tolist()
+        prewtgid = pre['wtgid'].tolist()
+        postfdatatime = post['fdatatime'].tolist()
+        prefdatatime = pre['fdatatime'].tolist()
+        UCL = post['UCL'].tolist()
+        RESULT = post['RESULT'].tolist()
+        Warning = post['Warning'].tolist()
+        for i in range(len(pre) - 2):
+            if (prefdatatime[i] != postfdatatime[i]):
+                postfarmid.insert(i, prefarmid[i])
+                postfdatatime.insert(i, prefdatatime[i])
+                postwtgid.insert(i, prewtgid[i])
+                UCL.insert(i, 'null')
+                RESULT.insert(i, 'null')
+                Warning.insert(i, '无法判断')
+        result = {'farmid': postfarmid, 'wtgid': postwtgid, 'fdatatime': postfdatatime, 'UCL': UCL, 'RESULT': RESULT,
+                  'Warning': Warning}
+        result = pd.DataFrame(result)
+        return result
+
+    def extractData(self, DataFrameData, params):
+        data = {}
+        data2Dict = DataFrameData
+        for param in params:
+            data[param] = data2Dict[param]
+        name = data[params[0]].tolist()
+        num = data[params[1]].tolist()
+        time = data[params[2]].tolist()
+        Wind_name = name
+        Wind_num = num
+        name = str(Wind_name[0]) + "_" + str(Wind_num[0])
+        data = pd.DataFrame(data)
+        data = data.drop(['farmid', 'wtgid', 'fdatatime'], axis=1).values.astype('float32')
+        return name, time, data, Wind_name, Wind_num
+
+    def extractJson(self, jsonData, params, name, num):
+        json2Dict = json.loads(jsonData)
+        name = name + "-" + num
+        # time = json2Dict[params[2]]
+        data = {}
+        for param in params:
+            data[param] = json2Dict[param]
+        data = pd.DataFrame(data)
+        data = data.dropna(axis=0)
+        time = data[params[0]].tolist()
+        data = data.drop(columns=[params[0]]).values.astype('float32')
+        return name, time, data
+
+    def saveEWMA(self, data, savePath, fileName='AE'):
+        """
+        将EWMA结果存储为CSV文件
+        @param data: 数据结果。default={'TIME':[...],'UCL':[...],'RESULT':[...]}
+        @param savePath: EWMA结果保存路径
+        @param fileName: EWMA存储名称
+        @return: None
+        """
+        fileName += '_Ewma.csv'
+        self.mkDirs(path=savePath)
+        data = pd.DataFrame(data)
+        savePath = os.path.join(savePath, fileName)
+        self.saveCSV(data=data, savePath=savePath)
+
+    def saveCSV(self, data, savePath):
+        """
+        将pd.DataFrame结果存储为CSV文件
+        @param data: pd.DataFrame数据
+        @param savePath: CSV存储路径
+        @return: None
+        """
+        data.to_csv(savePath, index=False, encoding='gbk')
+        # print('========== CSV文件保存在', savePath + ' ==========')
+
+    def saveModel(self, model, savePath, fileName='AE'):
+        """
+        保存训练好的模型
+        @param model: 训练好的模型
+        @param savePath: 模型存储路径
+        @param fileName: 模型名称
+        @return: None
+        """
+        fileName += '_Model.h5'
+        self.mkDirs(path=savePath)
+        savePath = os.path.join(savePath, fileName)
+        model.save_weights(savePath)
+        # print("========== 模型训练完毕，保存在", savePath, " ==========")
+
+    def saveScaler(self, scaler, savePath, fileName='AE'):
+        """
+        保存归一化文本
+        @param scaler: 归一化对象
+        @param savePath: 归一化文本存储路径
+        @param fileName: 归一化文本名称
+        @return:
+        """
+        fileName += '_Scaler.txt'
+        self.mkDirs(path=savePath)
+        savePath = os.path.join(savePath, fileName)
+        file = open(savePath, 'wb')
+        pickle.dump(scaler, file)
+        file.close()
+        # print("========== 归一化范围统计完毕，保存在", savePath, " ==========")
+
+    def saveConstant(self, constant, savePath, fileName='AE'):
+        """
+        保存统计量文本
+        @param constant:统计结果
+        @param savePath: 统计量文本存储路径
+        @param fileName: 统计量文本名称
+        @return:
+        """
+        fileName += '_Constant.txt'
+        self.mkDirs(path=savePath)
+        savePath = os.path.join(savePath, fileName)
+        file = open(savePath, 'w')
+        file.write(constant)
+        file.close()
+        # print("========== 均值&方差计算完毕，保存在：", savePath, " ==========")
+
+    def saveLastEwma(self, value, savePath):
+        """
+        存储最后一个值
+        @param value: 最后一个值
+        @param savePath: 存储路径
+        @return:
+        """
+        file = open(savePath, 'w')
+        file.write(str(value))
+        file.close()
+
+    # FIXME: 改用xml解析参数，废除这个方法
+    def loadParam(self, path='./conf/AE_Params.txt'):
+        """
+        加载用于训练与测试的参数
+        @param path: SCADA参数文本(用于训练与测试)存储路径。default='./conf/AE_Params.txt'
+        @return: ['时间', '齿轮箱高速轴前端温度', '齿轮箱高速轴后端温度', ...]
+        """
+        file = open(path, 'r', encoding='utf-8')
+        params = file.readlines()
+        params = list(map(lambda a: a.strip('\n'), params))
+        file.close()
+        return params
+
+    def loadModel(self, model, path):
+        """
+        加载模型
+        @param path:模型存储路径
+        @return: 模型对象
+        """
+        model.load_weights(path)
+        return model
+
+    def loadScaler(self, path):
+        """
+        加载归一化文本
+        @param path: 归一化文本路径
+        @return: 归一化对象
+        """
+        file = open(path, 'rb')
+        scaler = pickle.load(file)
+        file.close()
+        return scaler
+
+    def loadConstant(self, path):
+        """
+        加载统计量文本
+        @param path: 统计量文本路径
+        @return: 均值,标准差
+        """
+        file = open(path, 'r')
+        constant = file.readline()
+        constant = constant.split(',')
+        mean = float(constant[0])
+        std = float(constant[1])
+        file.close()
+        return mean, std
+
+    def loadLastEwma(self, path):
+        """
+        加载最后一个值
+        @param path: 最后一个值的存储路径
+        @return: 最后一个值
+        """
+        file = open(path, 'r')
+        constant = file.readline()
+        value = float(constant)
+        file.close()
+        return value
+
+    def jsonWrapper(self, dict):
+        """
+        将字典包装成json对象
+        @param dict:字典
+        @return: json对象
+        """
+        # print(' 测试结束，返回测试结果（TIME:时间 UCL:阈值上限 RESULT:结果）')
+        return json.dumps(dict)
+
+    # def saveLastEwma(self, theLastEwma, savePath, fileName='AE V1.0'):
+    #     """
+    #     保存统计量文本
+    #     @param constant:统计结果
+    #     @param savePath: 统计量文本存储路径
+    #     @param fileName: 统计量文本名称
+    #     @return:
+    #     """
+    #     fileName += '_LastEwma.txt'
+    #     savePath = os.path.join(savePath, fileName)
+    #     file = open(savePath, 'w')
+    #     file.write(theLastEwma)
+    #     file.close()
+    #
+    # def loadLastEwma(self, savePath='./conf', fileName='AE V1.0'):
+    #     """
+    #     加载ewma文本
+    #     @param path: ewma文本路径
+    #     @return: 最后一个ewma值
+    #     """
+    #     fileName += '_LastEwma.txt'
+    #     path = savePath
+    #     savePath = os.path.join(savePath, fileName)
+    #     if (os.path.exists(savePath)):
+    #         file = open(savePath, 'r')
+    #         if (os.path.getsize(savePath)):
+    #             LastEwma = file.readline()
+    #             LastEwma = LastEwma.split(',')
+    #             theLastEwma = LastEwma[0]
+    #             file.close()
+    #             return theLastEwma
+    #         else:
+    #             file.close()
+    #             return 0
+    #     else:
+    #         self.mkDirs(path=path)
+    #         path = os.path.join(path, fileName)
+    #         return 0
+
+
+ioLib = IOLib()

TensorFlow_eaxmple/Model_train_test/condition_monitoring/lib/__init__.py

@@ -0,0 +1,9 @@
+# _*_ coding: UTF-8 _*_
+
+
+'''
+@Author : dingjiawen
+@Date : 2022/7/7 11:06
+@Usage : 
+@Desc :
+'''

TensorFlow_eaxmple/Model_train_test/datadeal/labeled_and_piece.py

@@ -0,0 +1,280 @@
+import numpy as np
+import tensorflow as tf
+from sklearn.model_selection import train_test_split
+import math
+
+'''
+class ConcatLabel：
+这是一个给data加上label的类
+使用示例：ConcatLabel(data, label,if_load)
+作用是按是否采用加载将data文件分别添加上label
+需要传入的参数有：
+data:要添加label的data  是一个list类型或者np.array
+label:添加的label为几   最好是int类型
+if_load:是否采用按加载来分  true则采用，false则不采用   布尔类型
+'''
+
+
+class ConcatLabel(object):
+
+    def __init__(self, data, label, if_load):
+        self.data = data
+        self.if_load = if_load
+        self.label = label
+
+    def __call__(self, *args, **kwargs):
+        data = np.array(self.data)
+        if not self.if_load:
+            length, dim = data.shape
+            label_list = [[self.label] for i in range(length)]
+            label_list = np.array(label_list)
+            label_list = tf.cast(label_list, dtype=tf.float32)
+            dataWithLabel = tf.concat([data, label_list], axis=1)
+            return dataWithLabel
+        height, length, dim = data.shape
+        data_new = tf.reshape(data, [height * length, dim])
+        label_list = [[self.label] for i in range(height * length)]
+        label_list = np.array(label_list)
+        dataWithLabel = tf.concat([data_new, label_list], axis=1)
+        dataWithLabel = tf.reshape(dataWithLabel, [height, length, dim + 1])
+        return dataWithLabel
+
+
+class GetLabel(object):
+
+    def __init__(self, data, label, if_load, if_2D=True):
+        self.data = data
+        self.if_load = if_load
+        self.label = label
+        self.if_2D = if_2D
+
+    def __call__(self, *args, **kwargs):
+        data = np.array(self.data)
+        if not self.if_load:
+            if self.if_2D:
+                data = DimensionChange(data, self.if_2D)
+                data = data(data, self.if_2D)
+                data = np.array(data)
+                # print(data)
+            epochs, lengths1, lengths2, dims = data.shape
+            label_list = np.empty([epochs, 1], dtype=np.float32)
+            for epoch in range(epochs):
+                label_list[epoch] = self.label
+            label_list = np.array(label_list)
+            withLabel = tf.cast(label_list, dtype=tf.float32)
+
+            print("dataWithLabel.shape:", withLabel.shape)
+            return (data, withLabel)
+        height, length, dim = data.shape
+        data_new = tf.reshape(data, [height * length, dim])
+        label_list = [[self.label] for i in range(height * length)]
+        label_list = np.array(label_list)
+        dataWithLabel = tf.concat([data_new, label_list], axis=1)
+        dataWithLabel = tf.reshape(dataWithLabel, [height, length, dim + 1])
+        return dataWithLabel
+
+
+'''
+class PieceAndBag：
+这是一个给data分成train_data和test_data的类
+使用示例：ConcatLabel(data, if_load)
+作用是按是否采用加载将data文件分成(train_data,train_label和(test_data,test_label)
+需要传入的参数有：
+data:要添加label的data  是一个list类型或者np.array
+if_load:是否采用按加载来分  true则采用，false则不采用   布尔类型
+'''
+
+
+class PieceAndBag(object):
+
+    def __init__(self, data, if_load):
+        self.data = data
+        self.if_load = if_load
+
+    def __call__(self, *args, **kwargs):
+        data = self.data
+        data = np.array(data)
+        print(data)
+        # print(data.shape)
+        test_data = []
+        train_data = []
+        test_label = []
+        train_label = []
+        if not self.if_load:
+            heigth, dim = data.shape
+            for i in range(heigth):
+                if i % 6 == 0:
+                    test_data.append(data[i, :-1])
+                    test_label.append(data[i, -1])
+                else:
+                    train_data.append(data[i, :-1])
+                    train_label.append(data[i, -1])
+            train_data = np.array(train_data)
+            train_label = np.array(train_label)
+            test_data = np.array(test_data)
+            test_label = np.array(test_label)
+            print(train_data.shape, train_label.shape, test_data.shape, test_label.shape)
+            return (train_data, train_label), (test_data, test_label)
+        height, length, dim = data.shape
+        data = tf.reshape(data, [height * length, dim])
+        for i in range(height * length):
+            if i % 6 == 0:
+                test_data.append(data[i, :-1])
+                test_label.append(data[i, -1])
+            else:
+                train_data.append(data[i, :-1])
+                train_label.append(data[i, -1])
+        train_data = tf.reshape(np.array(train_data), [height, length, dim - 1])
+        train_label = tf.reshape(np.array(train_label), [height, length, 1])
+        test_data = tf.reshape(np.array(test_data), [height, length, dim - 1])
+        test_label = tf.reshape(np.array(test_label), [height, length, 1])
+        train_data = tf.transpose(train_data, [1, 2, 0])
+        train_label = tf.transpose(train_label, [1, 2, 0])
+        test_data = tf.transpose(test_data, [1, 2, 0])
+        test_label = tf.transpose(test_label, [1, 2, 0])
+        return (train_data, train_label), (test_data, test_label)
+
+
+class PieceAndBag1(object):
+
+    def __init__(self, data, if_load):
+        self.data = data
+        self.if_load = if_load
+
+    def __call__(self, *args, **kwargs):
+        data = self.data
+        data = np.array(data)
+        # print(data)
+        print(data.shape)
+        test_data = []
+        train_data = []
+        test_label = []
+        train_label = []
+        if not self.if_load:
+            epochs, heigths, dim = data.shape
+            data_all = tf.slice(data, [0, 0, 0], [epochs, heigths, dim - 1])
+            label_all = tf.slice(data, [0, 0, dim - 1], [epochs, heigths, 1])
+
+            data_all = np.array(data_all)
+            label_all = np.array(label_all)
+
+            print("data.shape：", data_all.shape, "label.shape:", label_all.shape)
+            (train_data, test_data, train_label, test_label) = train_test_split(data_all, label_all, test_size=0.25,
+                                                                                shuffle=True, random_state=100)
+
+            train_data = np.array(train_data)
+            train_label = np.array(train_label)
+            test_data = np.array(test_data)
+            test_label = np.array(test_label)
+            print("train_data.shape:", train_data.shape, "train_label.shape:", train_label.shape, "test_data.shape:",
+                  test_data.shape, "test_label.shape:", test_label.shape)
+            return (train_data, train_label), (test_data, test_label)
+        height, length, dim = data.shape
+        data = tf.reshape(data, [height * length, dim])
+        for i in range(height * length):
+            if i % 6 == 0:
+                test_data.append(data[i, :-1])
+                test_label.append(data[i, -1])
+            else:
+                train_data.append(data[i, :-1])
+                train_label.append(data[i, -1])
+        train_data = tf.reshape(np.array(train_data), [height, length, dim - 1])
+        train_label = tf.reshape(np.array(train_label), [height, length, 1])
+        test_data = tf.reshape(np.array(test_data), [height, length, dim - 1])
+        test_label = tf.reshape(np.array(test_label), [height, length, 1])
+        train_data = tf.transpose(train_data, [1, 2, 0])
+        train_label = tf.transpose(train_label, [1, 2, 0])
+        test_data = tf.transpose(test_data, [1, 2, 0])
+        test_label = tf.transpose(test_label, [1, 2, 0])
+        return (train_data, train_label), (test_data, test_label)
+
+
+class PieceAndBag_new(object):
+
+    def __init__(self, data, label, if_load):
+        self.data = data
+        self.label = label
+        self.if_load = if_load
+
+    def __call__(self, *args, **kwargs):
+        data = self.data
+        data = np.array(data)
+
+        label = self.label
+        label = np.array(label)
+
+        # print(data)
+
+        test_data = []
+        train_data = []
+        test_label = []
+        train_label = []
+        if not self.if_load:
+            #epochs, heigths, dim = data.shape
+
+            print("data.shape：", data.shape, "label.shape:", label.shape)
+            (train_data, test_data, train_label, test_label) = train_test_split(data, label, test_size=0.125,
+                                                                                shuffle=True, random_state=100)
+
+            train_data = np.array(train_data)
+            train_label = np.array(train_label)
+            test_data = np.array(test_data)
+            test_label = np.array(test_label)
+            print("train_data.shape:", train_data.shape, "train_label.shape:", train_label.shape, "test_data.shape:",
+                  test_data.shape, "test_label.shape:", test_label.shape)
+            return (train_data, train_label), (test_data, test_label)
+        height, length, dim = data.shape
+        data = tf.reshape(data, [height * length, dim])
+        for i in range(height * length):
+            if i % 6 == 0:
+                test_data.append(data[i, :-1])
+                test_label.append(data[i, -1])
+            else:
+                train_data.append(data[i, :-1])
+                train_label.append(data[i, -1])
+        train_data = tf.reshape(np.array(train_data), [height, length, dim - 1])
+        train_label = tf.reshape(np.array(train_label), [height, length, 1])
+        test_data = tf.reshape(np.array(test_data), [height, length, dim - 1])
+        test_label = tf.reshape(np.array(test_label), [height, length, 1])
+        train_data = tf.transpose(train_data, [1, 2, 0])
+        train_label = tf.transpose(train_label, [1, 2, 0])
+        test_data = tf.transpose(test_data, [1, 2, 0])
+        test_label = tf.transpose(test_label, [1, 2, 0])
+        return (train_data, train_label), (test_data, test_label)
+
+
+class DimensionChange(object):
+
+    def __init__(self, data, if_2D, if_label=False):
+        self.data = data
+        self.if_2D = if_2D
+        self.if_label = if_label
+
+    def __call__(self, *args, **kwargs):
+        data = self.data
+        if_2D = self.if_2D
+        if_label = self.if_label
+
+        data = np.transpose(data, [0, 2, 1])
+        epochs, height, dim = data.shape
+        if if_2D:
+            if if_label:
+                print(data)
+                for epoch in range(epochs):
+                    data = tf.broadcast_to(data, [epoch * 32, height, dim])
+                print(data.shape)
+                return data
+            dim2D = int(height / 32)
+            data = np.reshape(data, [epochs, 32, dim2D, dim])
+            data = np.reshape(data, [epochs * 32, dim2D, dim])
+            data = np.reshape(data, [epochs * 32, int(math.sqrt(dim2D)), int(math.sqrt(dim2D)), dim])
+
+        #print(data.shape)
+        return data
+
+# if __name__ == '__main__':
+# data0 = loadData.DataDeal3(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
+# data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'))
+# dataWithLabel=ConcatLabel1(data,1.000,False)
+# dataWithLabel9 = dataWithLabel(data, 1.000, False)
+# print(dataWithLabel9)

TensorFlow_eaxmple/Model_train_test/datadeal/loadData.py

@@ -0,0 +1,247 @@
+import numpy as np
+import os
+import tensorflow as tf
+
+'''
+class DataDeal1：
+这是一个处理data的类
+使用示例：DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+作用是将后缀为.dat的文件按节点个数(head)和加载的点数n的不同解析为(3,108,dim/108)维的文件
+需要传入的参数有：
+head:节点头数   int类型
+filename:放置.dat文件的根路径   string类型
+'''
+
+
+class DataDeal1(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        result = [[[] * self.head] * k.shape[0]]
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum0 = [[] for i in range(self.head)]
+        sum1 = [[] for i in range(self.head)]
+        sum2 = [[] for i in range(self.head)]
+        '''sum0=[None]*self.head
+        sum1 = [None] * self.head
+        sum2 = [None] * self.head
+        for i in range(self.head):
+            sum0[i] = [0]* 204800
+            sum1[i] = [0]* 204800
+            sum2[i] = [0]* 204800'''
+        z = 0
+        for filename in Z:
+            # 将二进制文件读成np数组
+            each_data = np.fromfile(filename, dtype=np.float32)
+            data = np.array(each_data)
+            # 创建一个二维空数组
+            data_true = [[] for i in range(self.head)]
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            for i in range(self.head):
+                data_true[i] = data[i::self.head]
+                # 如果第一次是空数剧，则无法进行拼接
+                if z == 0:
+                    sum0[i] = data_true[i]
+                if z == 4:
+                    sum1[i] = data_true[i]
+                if z == 8:
+                    sum2[i] = data_true[i]
+            # 将二维空数组重新整合成np.array的形式
+            data_true = np.array(data_true)
+            if z % 12 == 0 or z % 12 == 1 or z % 12 == 2 or z % 12 == 3:
+                if z != 0:
+                    sum0 = np.vstack((sum0, data_true))
+            if z % 12 == 4 or z % 12 == 6 or z % 12 == 7 or z % 12 == 5:
+                if z != 4:
+                    sum1 = np.vstack((sum1, data_true))
+            if z % 12 == 9 or z % 12 == 10 or z % 12 == 11 or z % 12 == 8:
+                if z != 8:
+                    sum2 = np.vstack((sum2, data_true))
+            z = z + 1
+        sum0 = np.array(sum0)
+        sum1 = np.array(sum1)
+        sum2 = np.array(sum2)
+        print("sum0:", sum0.shape)
+        print("sum1:", sum1.shape)
+        print("sum2:", sum2.shape)
+        return sum0, sum1, sum2
+
+
+'''
+class DataDeal2：
+这是一个处理data的类
+使用示例：DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+作用是将后缀为.dat的文件按节点个数(head)解析为(3*108,dim/108)维的文件
+需要传入的参数有：
+head:节点头数   int类型
+filename:放置.dat文件的根路径   string类型
+'''
+
+
+class DataDeal2(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum = [[] for i in range(self.head)]
+        z = 0
+        for filename in Z:
+            # 将二进制文件读成np数组
+            each_data = np.fromfile(filename, dtype=np.float32)
+            data = np.array(each_data)
+            # 创建一个二维空数组
+            data_true = [[] for i in range(self.head)]
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            for i in range(self.head):
+                data_true[i] = data[i::self.head]
+                # 如果第一次是空数剧，则无法进行拼接
+                if z == 0:
+                    sum[i] = data_true[i]
+            # 将二维空数组重新整合成np.array的形式
+            data_true = np.array(data_true)
+
+            if z != 0:
+                sum = np.vstack((sum, data_true))
+            z = z + 1
+        sum = np.array(sum)
+        print("sum:", sum.shape)
+        return sum
+
+
+class DataDeal3(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum = [[] for i in range(self.head)]
+        z = 0
+        for filename in Z:
+            # 将二进制文件读成np数组
+            each_data = np.fromfile(filename, dtype=np.float32)
+            data = np.array(each_data)
+            # 创建一个二维空数组
+            data_true = [[] for i in range(self.head)]
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            for i in range(self.head):
+                data_true[i] = data[i::self.head]
+                # 如果第一次是空数剧，则无法进行拼接
+                if z == 0:
+                    sum[i] = data_true[i]
+            if z==0:
+                print(data_true)
+            # 将二维空数组重新整合成np.array的形式
+            data_true = np.array(data_true)
+            data_new = np.expand_dims(data_true, axis=0)
+
+
+            if z == 0:
+                sum = np.expand_dims(sum, axis=0)
+            else:
+                sum = np.vstack((sum, data_new))
+            z = z + 1
+        sum = np.array(sum)
+        print("sum:", sum.shape)
+
+        return sum
+
+class DataDeal4(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum = [[] for i in range(self.head)]
+        z = 0
+        for filename in Z:
+            # 将二进制文件读成np数组
+            each_data = np.fromfile(filename, dtype=np.float32)
+            data = np.array(each_data)
+            (dims,)=data.shape
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            data=np.reshape(data,[self.head,int(dims/self.head)])
+
+            data_new = np.expand_dims(data, axis=0)
+
+            if z == 0:
+                sum = data_new
+            else:
+                sum = np.vstack((sum, data_new))
+            z = z + 1
+        sum = np.array(sum)
+        print("sum:", sum.shape)
+
+        return sum
+
+if __name__ == '__main__':
+    data0 = DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'))
+    print(data)
+    # a = np.array([[1, 2,5], [3, 4,7]])
+    # print(a)
+    # b=np.expand_dims(a,axis=0)
+    # print(b)
+    # b=np.transpose(b,[0,2,1])
+    # print(b)

TensorFlow_eaxmple/Model_train_test/datadeal/saveData.py

@@ -0,0 +1,84 @@
+import tensorflow as tf
+
+from datadeal import labeled_and_piece, loadData
+import numpy as np
+
+# 导入数据，并进行数据处理
+# 导入第一类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
+data1 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'))
+dataWithLabel1 = labeled_and_piece.GetLabel(data1, 0.000, False)
+(data1, dataWithLabel1) = dataWithLabel1(data1, 0.000, False)
+
+# 导入第二类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+data2 = np.array(data0(9, 'E:\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
+dataWithLabel2 = labeled_and_piece.GetLabel(data2, 1.000, False)
+(data2, dataWithLabel2) = dataWithLabel2(data2, 1.000, False)
+
+# 导入第三类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA')
+data3 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA'))
+dataWithLabel3 = labeled_and_piece.GetLabel(data3, 2.000, False)
+(data3, dataWithLabel3) = dataWithLabel3(data3, 2.000, False)
+
+# 导入第四类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA')
+data4 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA'))
+dataWithLabel4 = labeled_and_piece.GetLabel(data4, 3.000, False)
+(data4, dataWithLabel4) = dataWithLabel4(data4, 3.000, False)
+
+# 导入第五类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA')
+data5 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA'))
+dataWithLabel5 = labeled_and_piece.GetLabel(data5, 4.000, False)
+(data5, dataWithLabel5) = dataWithLabel5(data5, 4.000, False)
+
+# 导入第六类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA')
+data6 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA'))
+dataWithLabel6 = labeled_and_piece.GetLabel(data6, 5.000, False)
+(data6, dataWithLabel6) = dataWithLabel6(data6, 5.000, False)
+
+# 导入第七类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA')
+data7 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA'))
+dataWithLabel7 = labeled_and_piece.GetLabel(data7, 6.000, False)
+(data7, dataWithLabel7) = dataWithLabel7(data7, 6.000, False)
+
+# 导入第八类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA')
+data8 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA'))
+dataWithLabel8 = labeled_and_piece.GetLabel(data8, 7.000, False)
+(data8, dataWithLabel8) = dataWithLabel8(data8, 7.000, False)
+
+# 导入第九类故障并打标签
+data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA')
+data9 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA'))
+dataWithLabel9 = labeled_and_piece.GetLabel(data9, 8.000, False)
+(data9, dataWithLabel9) = dataWithLabel9(data9, 8.000, False)
+
+# 合并
+data_all = tf.concat([data1, data2, data3, data4, data5, data6, data7, data8, data9], axis=0)
+label_all = tf.concat(
+    [dataWithLabel1, dataWithLabel2, dataWithLabel3, dataWithLabel4, dataWithLabel5, dataWithLabel6, dataWithLabel7,
+     dataWithLabel8, dataWithLabel9], axis=0)
+# print("data_all",data_all)
+# print("label_all：",label_all)
+# data_all = np.array(data_all)
+
+# 划分训练集和测试集,并打乱
+data_new = labeled_and_piece.PieceAndBag_new(data_all, label_all, False)
+(train_data, train_label), (test_data, test_label) = data_new(data_all, label_all, False)
+np.save("train_data.npy",train_data)
+np.save("train_label.npy",train_label)
+np.save("test_data.npy",test_data)
+np.save("test_label.npy",test_label)
+
+# confusion_matrix(test_label,y_pred=?)  //混淆矩阵的得法
+# TSNE.fit_transform()  //T-SNE降维表示
+
+'''train_data.shape: (7776, 80, 80, 9)
+train_label.shape: (7776, 1)
+test_data.shape: (2592, 80, 80, 9)
+test_label.shape: (2592, 1)'''

TensorFlow_eaxmple/Model_train_test/model/ResNet/train/resnet.py

@@ -0,0 +1,133 @@
+import tensorflow as tf
+
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.manifold import TSNE
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+
+# 数据读入
+train_data = np.load("../../../data/train_data.npy")
+train_label = np.load("../../../data/train_label.npy")
+test_data = np.load("../../../data/test_data.npy")
+test_label = np.load("../../../data/test_label.npy")
+
+
+# CIFAR_100_data = tf.keras.datasets.cifar100
+# (train_data, train_label), (test_data, test_label) = CIFAR_100_data.load_data()
+# train_data=np.array(train_data)
+# train_label=np.array(train_label)
+# print(train_data.shape)
+# print(train_label.shape)
+# print(train_data)
+# print(test_data)
+#
+#
+def identity_block(input_tensor, out_dim):
+    con1 = tf.keras.layers.Conv2D(filters=out_dim // 4, kernel_size=1, padding='SAME', activation=tf.nn.relu)(
+        input_tensor)
+    bhn1 = tf.keras.layers.BatchNormalization()(con1)
+
+    con2 = tf.keras.layers.Conv2D(filters=out_dim // 4, kernel_size=3, padding='SAME', activation=tf.nn.relu)(bhn1)
+    bhn2 = tf.keras.layers.BatchNormalization()(con2)
+
+    con3 = tf.keras.layers.Conv2D(filters=out_dim, kernel_size=1, padding='SAME', activation=tf.nn.relu)(bhn2)
+
+    out = tf.keras.layers.Add()([input_tensor, con3])
+    out = tf.nn.relu(out)
+
+    return out
+
+
+def resnet_Model():
+    inputs = tf.keras.Input(shape=[80, 80, 9])
+    conv1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='SAME', activation=tf.nn.relu)(inputs)
+    '''第一层'''
+    output_dim = 64
+    identity_1 = tf.keras.layers.Conv2D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        conv1)
+    identity_1 = tf.keras.layers.BatchNormalization()(identity_1)
+    for _ in range(3):
+        identity_1 = identity_block(identity_1, output_dim)
+    '''第二层'''
+    output_dim = 128
+    identity_2 = tf.keras.layers.Conv2D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_1)
+    identity_2 = tf.keras.layers.BatchNormalization()(identity_2)
+    for _ in range(2):
+        identity_2 = identity_block(identity_2, output_dim)
+    '''第三层'''
+    output_dim = 256
+    identity_3 = tf.keras.layers.Conv2D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_2)
+    identity_3 = tf.keras.layers.BatchNormalization()(identity_3)
+    for _ in range(3):
+        identity_3 = identity_block(identity_3, output_dim)
+    '''第四层'''
+    output_dim = 512
+    identity_4 = tf.keras.layers.Conv2D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_3)
+    identity_4 = tf.keras.layers.BatchNormalization()(identity_4)
+    for _ in range(3):
+        identity_4 = identity_block(identity_4, output_dim)
+    flatten = tf.keras.layers.Flatten()(identity_4)
+    dropout = tf.keras.layers.Dropout(0.217)(flatten)
+
+    dense = tf.keras.layers.Dense(128, activation=tf.nn.relu,)(dropout)
+    dense = tf.keras.layers.BatchNormalization(name="bn_last")(dense)
+    dense = tf.keras.layers.Dense(9, activation=tf.nn.softmax)(dense)
+    model = tf.keras.Model(inputs=inputs, outputs=dense)
+    return model
+
+
+if __name__ == '__main__':
+    model = resnet_Model()
+    model.compile(optimizer=tf.optimizers.SGD(1e-3,momentum=0.02), loss=tf.losses.sparse_categorical_crossentropy,
+                         metrics=['acc'])
+    model.summary()
+    history = model.fit(train_data, train_label, epochs=10, batch_size=10,validation_data=(test_data, test_label))
+    model.save("ResNet_model.h5")
+    # model=tf.keras.models.load_model("../model/ResNet_model.h5")
+    #
+    # trained_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('bn_last').output).predict(
+    #     train_data)
+    # predict_label = model.predict(test_data)
+    # predict_label_max = np.argmax(predict_label, axis=1)
+    # predict_label = np.expand_dims(predict_label_max, axis=1)
+    #
+    # confusion_matrix = confusion_matrix(test_label, predict_label)
+
+    tsne = TSNE(n_components=3, verbose=2, perplexity=30,n_iter=5000).fit_transform(trained_data)
+    print("tsne[:,0]", tsne[:, 0])
+    print("tsne[:,1]", tsne[:, 1])
+    print("tsne[:,2]", tsne[:, 2])
+    x, y, z = tsne[:, 0], tsne[:, 1], tsne[:, 2]
+    x = (x - np.min(x)) / (np.max(x) - np.min(x))
+    y = (y - np.min(y)) / (np.max(y) - np.min(y))
+    z = (z - np.min(z)) / (np.max(z) - np.min(z))
+
+    fig1 = plt.figure()
+    ax1 = fig1.add_subplot(projection='3d')
+    ax1.scatter3D(x, y, z, c=train_label, cmap=plt.cm.get_cmap("jet", 10))
+
+    fig2 = plt.figure()
+    ax2 = fig2.add_subplot()
+    sns.heatmap(confusion_matrix, annot=True, fmt="d", cmap='Blues')
+    plt.ylabel('Actual label')
+    plt.xlabel('Predicted label')
+
+
+    # fig3 = plt.figure()
+    # ax3 = fig3.add_subplot()
+    # plt.plot(history.epoch, history.history.get('acc'), label='acc')
+    # plt.plot(history.epoch, history.history.get('val_acc'), label='val_acc')
+    #
+    # fig4 = plt.figure()
+    # ax4 = fig3.add_subplot()
+    # plt.plot(history.epoch, history.history.get('loss'), label='loss')
+    # plt.plot(history.epoch, history.history.get('val_loss'), label='val_loss')
+    plt.legend()
+    plt.show()
+
+    score = model.evaluate(test_data, test_label)
+    print('score:', score)

TensorFlow_eaxmple/Model_train_test/model/VGG/train/VGG.py

@@ -0,0 +1,127 @@
+import tensorflow as tf
+import numpy as np
+import pandas
+from matplotlib import pyplot as plt
+from sklearn.manifold import TSNE
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+import math
+# import printfile as pf
+
+# 数据读入
+# train_data = np.load("../../../data/train_data.npy")
+# train_label = np.load("../../../data/train_label.npy")
+test_data = np.load("../../../data/test_data.npy")
+test_label = np.load("../../../data/test_label.npy")
+
+
+# 构建模型
+def VGG_Model():
+    inputs = tf.keras.Input(shape=[80, 80, 9])
+    # Convolution layer (VGG)
+    inner = tf.keras.layers.Conv2D(64, kernel_size=3, name='conv1', kernel_initializer='he_normal')(
+        inputs)  # (None, 128, 64, 64)
+    inner = tf.keras.layers.BatchNormalization()(inner)
+    inner = tf.keras.layers.Activation('relu')(inner)
+    inner = tf.keras.layers.MaxPooling2D(name='max1')(inner)  # (None,64, 32, 64)
+
+    inner = tf.keras.layers.Conv2D(128, kernel_size=3, name='conv2', kernel_initializer='he_normal')(
+        inner)  # (None, 64, 32, 128)
+    inner = tf.keras.layers.BatchNormalization()(inner)
+    inner = tf.keras.layers.Activation('relu')(inner)
+    inner = tf.keras.layers.MaxPooling2D(name='max2')(inner)  # (None, 32, 16, 128)
+
+    inner = tf.keras.layers.Conv2D(256, kernel_size=3, name='conv3', kernel_initializer='he_normal')(
+        inner)  # (None, 32, 16, 256)
+    inner = tf.keras.layers.BatchNormalization()(inner)
+    inner = tf.keras.layers.Activation('relu')(inner)
+    inner = tf.keras.layers.Conv2D(256, kernel_size=3, name='conv4', kernel_initializer='he_normal')(
+        inner)  # (None, 32, 16, 256)
+    inner = tf.keras.layers.BatchNormalization()(inner)
+    inner = tf.keras.layers.Activation('relu')(inner)
+    inner = tf.keras.layers.MaxPooling2D(name='max3')(inner)  # (None, 32, 8, 256)
+
+    inner = tf.keras.layers.Conv2D(512, kernel_size=3, name='conv5', kernel_initializer='he_normal')(
+        inner)  # (None, 32, 8, 512)
+    inner = tf.keras.layers.BatchNormalization()(inner)
+    inner = tf.keras.layers.Activation('relu')(inner)
+    inner = tf.keras.layers.Conv2D(512, kernel_size=3, name='conv6')(inner)  # (None, 32, 8, 512)
+    inner = tf.keras.layers.BatchNormalization()(inner)
+    inner = tf.keras.layers.Activation('relu')(inner)
+    inner = tf.keras.layers.MaxPooling2D(name='max4')(inner)  # (None, 32, 4, 512)
+
+    inner = tf.keras.layers.Conv2D(512, kernel_size=1, kernel_initializer='he_normal', name='con7')(
+        inner)  # (None, 32, 4, 512)
+    inner = tf.keras.layers.BatchNormalization()(inner)
+    inner = tf.keras.layers.Activation('relu')(inner)
+
+    flatten = tf.keras.layers.Flatten()(inner)
+
+    dropout = tf.keras.layers.Dropout(0.217)(flatten)
+
+    dense = tf.keras.layers.Dense(128, activation=tf.nn.relu)(dropout)
+    dense = tf.keras.layers.BatchNormalization(name='bn_last')(dense)
+
+    outputs = tf.keras.layers.Dense(9, activation=tf.nn.softmax)(dense)
+    model = tf.keras.Model(inputs=inputs, outputs=outputs)
+
+    return model
+
+
+if __name__ == '__main__':
+    model = VGG_Model()
+
+    model.compile(optimizer=tf.optimizers.SGD(1e-3, momentum=0.02), loss=tf.losses.sparse_categorical_crossentropy,
+                  metrics=['acc'])
+    model.summary()
+
+    # history = model.fit(train_data, train_label, epochs=10, batch_size=10, validation_split=0.2)
+    # model.save("VGG_model.h5")
+    model=tf.keras.models.load_model('../model/VGG_model.h5')
+
+
+
+
+
+    # trained_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('bn_last').output).predict(
+    #     train_data)
+    predict_label=model.predict(test_data)
+    predict_label_max=np.argmax(predict_label,axis=1)
+    predict_label=np.expand_dims(predict_label_max,axis=1)
+    print(predict_label.shape)
+    print(predict_label)
+    # confusion_matrix=confusion_matrix(test_label,predict_label)
+    #
+    #
+    # tsne = TSNE(n_components=3, verbose=2, perplexity=30).fit_transform(trained_data)
+    # print("tsne[:,0]",tsne[:,0])
+    # print("tsne[:,1]",tsne[:,1])
+    # print("tsne[:,2]",tsne[:,2])
+    # x, y, z = tsne[:, 0], tsne[:, 1], tsne[:, 2]
+    # x = (x - np.min(x)) / (np.max(x) - np.min(x))
+    # y = (y - np.min(y)) / (np.max(y) - np.min(y))
+    # z = (z - np.min(z)) / (np.max(z) - np.min(z))
+    #
+    # fig1 = plt.figure()
+    # ax1 = fig1.add_subplot(projection='3d')
+    #
+    # ax1.scatter3D(x, y, z, c=train_label, cmap=plt.cm.get_cmap("jet", 10))
+    #
+    #
+    # fig2=plt.figure()
+    # ax2=fig2.add_subplot()
+    # sns.heatmap(confusion_matrix,annot=True,fmt="d",cmap='Blues')
+    # plt.ylabel('Actual label')
+    # plt.xlabel('Predicted label')
+    # plt.show()
+
+
+
+    # plt.plot(history.epoch, history.history.get('acc'), label='acc')
+    # plt.plot(history.epoch, history.history.get('val_acc'), label='val_acc')
+    # plt.plot(history.epoch, history.history.get('loss'), label='loss')
+    # plt.plot(history.epoch, history.history.get('val_loss'), label='val_loss')
+    # plt.legend()
+    # plt.show()
+    # score = model.evaluate(test_data, test_label)
+    # print('score:', score)

TensorFlow_eaxmple/OpenCV_try/卷积核与图像特征提取.py

@@ -0,0 +1,22 @@
+import numpy as np
+import cv2
+from scipy import ndimage
+
+#一个3*3的卷积核，如果一个像素比他周围的像素更加突出，就会提升其本身的亮度
+kernel133=np.array([[-1,-1,-1],
+                    [-1,-8,-1],
+                    [-1,-1,-1]])
+
+#一个3*3的卷积核，如果一个像素比他周围的像素更加昏暗，就会更进一步减少其本身的亮度
+kernel133_D=np.array([[1,1,1],
+                    [1,-8,1],
+                    [1,1,1]])
+
+img=cv2.imread("lana.jpg",0)
+#ndimage是一个处理多维图像的函数库，他可以用于图像滤波器，傅里叶变换，图像的旋转拉伸以及测量和形态学处理
+linghtImg=ndimage.convolve(img,kernel133_D)
+blurred=cv2.GaussianBlur(img,(11,11),0)
+gaussImg=img-blurred
+cv2.imshow("img",linghtImg)
+cv2.imshow("img1",gaussImg)
+cv2.waitKey(0)

TensorFlow_eaxmple/OpenCV_try/图像的扩缩裁挖.py

@@ -0,0 +1,12 @@
+import cv2
+
+
+img=cv2.imread("lana.jpg",0)
+
+
+img_new=cv2.resize(img,(1000,500))
+
+patch_tree=img_new[0:500,500:1000]
+img_new=cv2.resize(patch_tree,(500,500))
+cv2.imshow('test1',img_new)
+cv2.waitKey(0)

TensorFlow_eaxmple/OpenCV_try/图像的旋转平移和翻转.py

@@ -0,0 +1,9 @@
+import cv2
+import numpy as np
+img=cv2.imread('lena.jpg')
+# print(img)
+m_copy_img=np.array([[0,0.8,-100],[0.8,0,-12]],dtype=np.float32)
+img_change=cv2.warpAffine(img,m_copy_img,(1000,500))
+# cv2.imshow('test',img)
+cv2.imshow('test',img_change)
+cv2.waitKey(0)

TensorFlow_eaxmple/OpenCV_try/图像的读取与储存.py

@@ -0,0 +1,7 @@
+import cv2
+
+'''cv2调用了imread方法从当前目录下读取了文件，读取时转换为灰度图'''
+image = cv2.imread("attribute2-attribute3.png", cv2.IMREAD_GRAYSCALE)
+'''这里传递进了2个参数，第一个表示图片的存储名称，并在存储时图片的类型由png格式改变为jpg格式存储，第二个参数为内存中所要存储的目标'''
+'''imread()函数会删除所有图片alpha通道的信息，imwrite()函数要求输出的图片格式为BGR或者灰度图'''
+cv2.imwrite("code2.jpg", image)

TensorFlow_eaxmple/OpenCV_try/图像的转换.py

@@ -0,0 +1,10 @@
+import numpy as np
+import cv2
+
+image = np.mat(np.zeros((200, 200)))
+imageByteArray = bytearray(image)
+print(imageByteArray)
+#imageBGR = np.array(imageByteArray).reshape(200, 200) 报错？显示cannot reshape array of size 320000 into shape (200,200)
+imageBGR = np.array(imageByteArray).reshape(400, 800)
+cv2.imshow("cool", imageBGR)
+cv2.waitKey(0)

TensorFlow_eaxmple/OpenCV_try/图像的随机旋转变换.py

@@ -0,0 +1,8 @@
+import cv2
+
+img=cv2.imread('lena.jpg')
+rows,cols,depth=img.shape
+img_change= cv2.getRotationMatrix2D((cols/2,rows/2),45,1)
+res=cv2.warpAffine(img,img_change,(rows,cols))
+cv2.imshow('test',res)
+cv2.waitKey(0)

TensorFlow_eaxmple/OpenCV_try/图像的随机裁剪.py

@@ -0,0 +1,15 @@
+import cv2
+import random
+
+img=cv2.imread('lena.jpg')
+width,height,depth=img.shape
+img_width_box=width*0.2
+img_height_box=height*0.2
+print(img_width_box,img_height_box)
+for _ in range(9):
+    start_pointX=random.uniform(0,img_width_box)
+    start_pointY=random.uniform(0,img_height_box)
+    print(start_pointX,start_pointY)
+    copyImg=img[int(start_pointX):1000,int(start_pointY):800]
+    cv2.imshow('test',copyImg)
+    cv2.waitKey(0)

TensorFlow_eaxmple/OpenCV_try/图像色彩的随机变换.py

@@ -0,0 +1,14 @@
+import cv2
+import numpy as np
+import random
+img=cv2.imread('lena.jpg')
+img_hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
+turn_green_hsv=img_hsv.copy()
+
+'''等式左边的参数，方括号中第一个和第二个参数分别代表图像矩阵的坐标，第三个参数代表HSV的选择，0指的是色调，1指的是饱和度，2指的是明暗度'''
+turn_green_hsv[:,:,0]=(turn_green_hsv[:,:,0]+np.random.random())%180
+turn_green_hsv[:,:,1]=(turn_green_hsv[:,:,1]+np.random.random())%180
+turn_green_hsv[:,:,2]=(turn_green_hsv[:,:,2]+np.random.random())%180
+turn_green_img=cv2.cvtColor(turn_green_hsv,cv2.COLOR_HSV2BGR)
+cv2.imshow("test",turn_green_img)
+cv2.waitKey(0)

TensorFlow_eaxmple/OpenCV_try/图像色调的调整.py

@@ -0,0 +1,14 @@
+import cv2
+img=cv2.imread('lena.jpg')
+img_hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
+turn_green_hsv=img_hsv.copy()
+less_color_hsv=img_hsv.copy()
+'''等式左边的参数，方括号中第一个和第二个参数分别代表图像矩阵的坐标，第三个参数代表HSV的选择，0指的是色调，1指的是饱和度，2指的是明暗度'''
+turn_green_hsv[:,:,0]=(turn_green_hsv[:,:,0]-30)%180
+less_color_hsv[:,:,0]=less_color_hsv[:,:,0]*0.6
+#turn_green_hsv[:,:,1]=(turn_green_hsv[:,:,1]-30)%180
+#turn_green_hsv[:,:,2]=(turn_green_hsv[:,:,2]-30)%180
+turn_green_img1=cv2.cvtColor(less_color_hsv,cv2.COLOR_HSV2BGR)
+turn_green_img=cv2.cvtColor(turn_green_hsv,cv2.COLOR_HSV2BGR)
+cv2.imshow("test",turn_green_img1)
+cv2.waitKey(0)

TensorFlow_eaxmple/OpenCV_try/基本的图片读取.py

@@ -0,0 +1,20 @@
+import numpy as np
+import cv2
+
+'''生成一个每个具体数值均为0的一个[300,300]的矩阵'''
+'''从图片的角度，生成一个每个像素点均为黑色的一个300*3000的图片'''
+img = np.mat(np.zeros((3, 3), dtype=np.uint8))
+img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+print(img.shape)
+print(img)
+img[:, :, 0] = np.mat([[255, 0, 0], [255, 255, 0], [255, 128, 0]])
+img[:, :, 1] = np.mat([[0, 255, 0], [255, 0, 255], [255, 128, 0]])
+img[:, :, 2] = np.mat([[0, 0, 255], [0, 255, 255], [255, 128, 0]])
+print(img)
+'''这里是cv2输出图片的固定写法，将刚才生成的一个矩阵以图片的形式在使用者计算机输出端上显示出来'''
+cv2.imshow("test",img)
+'''之后waitKey方法要求输出的图像暂时等待，可以通过手动操作的形式取消图片显示，若去掉则显示之后迅速消失'''
+cv2.waitKey(0)
+
+
+# (255,0,0),(0,255,0),(0,0,255)

TensorFlow_eaxmple/OpenCV_try/对鼠标的监控.py

@@ -0,0 +1,25 @@
+import cv2
+
+
+def on_mouse(event, x, y, flags, param):
+    rect_start = (0, 0)
+    rect_end = (0, 0)
+    # 鼠标左键按下，抬起，双击
+    if event == cv2.EVENT_LBUTTONDOWN:
+        rect_start = (x, y)
+    elif event == cv2.EVENT_LBUTTONUP:
+        rect_end = (x, y)
+    cv2.rectangle(img, rect_start, rect_end, (0, 255, 0), 2)
+
+
+img = cv2.imread("lena.jpg")
+cv2.namedWindow('test')
+cv2.setMouseCallback("test", on_mouse)
+
+while (1):
+
+    cv2.imshow("test", img)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+cv2.destroyAllWindows()

TensorFlow_eaxmple/OpenCV_try/随机生成数组并显示出来.py

@@ -0,0 +1,7 @@
+import cv2
+import numpy as np
+import os
+randomByteArray=bytearray(os.urandom(120000))
+flatNumpArray=np.array(randomByteArray).reshape(300,400)
+cv2.imshow("cool",flatNumpArray)
+cv2.waitKey(0)

TensorFlow_eaxmple/TensorFlow_constant_Variable/1.py

@@ -0,0 +1,103 @@
+import tensorflow as tf
+import tensorflow.keras.layers as layers
+import tensorflow.losses
+import math
+
+
+def cross_entropy(Y, P):
+    sum = 0.0
+    for x in map(lambda y, p: (1 - y) * math.log(1 - p) + y * math.log(p), Y, P):
+        sum += x
+    return sum
+
+
+# 生成器模型
+def make_generator_model():
+    model = tf.keras.Sequential()
+    model.add(layers.Dense(7 * 7 * 256, use_bias=False, input_shape=(100,)))
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+
+    model.add(layers.Reshape((7, 7, 256)))
+    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
+
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+
+    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
+
+    model.add(layers.BatchNormalization())
+    model.add(layers.LeakyReLU())
+
+    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
+
+
+# 判别器模型
+def make_discriminator_model():
+    model = tf.keras.Sequential()
+    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
+                            input_shape=[28, 28, 1]))
+    model.add(layers.LeakyReLU())
+    model.add(layers.Dropout(0.3))
+
+    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
+    model.add(layers.LeakyReLU())
+    model.add(layers.Dropout(0.3))
+
+    model.add(layers.Flatten())
+    model.add(layers.Dense(1))
+
+    return model
+
+
+# 判别器损失函数
+def discriminator_loss(real_output, fake_output):
+    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
+    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
+    total_loss = real_loss + fake_loss
+    return total_loss
+
+
+# 生成器损失函数
+def generator_loss(fake_output):
+    return cross_entropy(tf.ones_like(fake_output), fake_output)
+
+
+# 生成器和判别器优化器
+generator_optimizer = tf.keras.optimizers.Adam(1e-4)
+discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
+
+
+# 训练过程定义
+def train_step(images):
+    BATCH_SIZE, noise_dim=images.get_shape().as_list[-1]
+    noise = tf.random.normal([BATCH_SIZE, noise_dim])
+
+    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
+        generated_images = generator(noise, training=True)
+
+        real_output = discriminator(images, training=True)
+        fake_output = discriminator(generated_images, training=True)
+
+        gen_loss = generator_loss(fake_output)
+        disc_loss = discriminator_loss(real_output, fake_output)
+
+    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
+    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
+
+    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
+    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
+
+
+# 开始训练
+def train(dataset, epochs):
+    for epoch in range(epochs):
+        start = time.time()
+
+        for image_batch in dataset:
+            train_step(image_batch)
+
+# 只对generator模型进行参数保存
+generator = make_generator_model()
+discriminator = make_discriminator_model()
+model.save_weights("./saver/model")

TensorFlow_eaxmple/TensorFlow_constant_Variable/2.py

@@ -0,0 +1,79 @@
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import numpy as np
+
+CIFAR_100_data = tf.keras.datasets.cifar100
+(train_data, train_label), (test_data, test_label) = CIFAR_100_data.load_data()
+'''train_data=np.array(train_data)
+train_label=np.array(train_data)
+print(train_data.shape)
+print(train_label.shape)'''
+
+
+
+def identity_block(input_tensor, out_dim):
+    con1 = tf.keras.layers.Conv2D(filters=out_dim // 4, kernel_size=1, padding='SAME', activation=tf.nn.relu)(
+        input_tensor)
+    bhn1 = tf.keras.layers.BatchNormalization()(con1)
+
+    con2 = tf.keras.layers.Conv2D(filters=out_dim // 4, kernel_size=3, padding='SAME', activation=tf.nn.relu)(bhn1)
+    bhn2 = tf.keras.layers.BatchNormalization()(con2)
+
+    con3 = tf.keras.layers.Conv2D(filters=out_dim, kernel_size=1, padding='SAME', activation=tf.nn.relu)(bhn2)
+
+    out = tf.keras.layers.Add()([input_tensor, con3])
+    out = tf.nn.relu(out)
+
+    return out
+
+
+def resnet_Model():
+    inputs = tf.keras.Input(shape=[32, 32, 3])
+    conv1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='SAME', activation=tf.nn.relu)(inputs)
+    '''第一层'''
+    output_dim = 64
+    identity_1 = tf.keras.layers.Conv2D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        conv1)
+    identity_1 = tf.keras.layers.BatchNormalization()(identity_1)
+    for _ in range(3):
+        identity_1 = identity_block(identity_1, output_dim)
+    '''第二层'''
+    output_dim = 128
+    identity_2 = tf.keras.layers.Conv2D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_1)
+    identity_2 = tf.keras.layers.BatchNormalization()(identity_2)
+    for _ in range(2):
+        identity_2 = identity_block(identity_2, output_dim)
+    '''第三层'''
+    output_dim = 256
+    identity_3 = tf.keras.layers.Conv2D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_2)
+    identity_3 = tf.keras.layers.BatchNormalization()(identity_3)
+    for _ in range(3):
+        identity_3 = identity_block(identity_3, output_dim)
+    '''第四层'''
+    output_dim = 512
+    identity_4 = tf.keras.layers.Conv2D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_3)
+    identity_4 = tf.keras.layers.BatchNormalization()(identity_4)
+    for _ in range(3):
+        identity_4 = identity_block(identity_4, output_dim)
+    flatten = tf.keras.layers.Flatten()(identity_4)
+    dropout = tf.keras.layers.Dropout(0.217)(flatten)
+    dense = tf.keras.layers.Dense(1024, activation=tf.nn.relu)(dropout)
+    dense = tf.keras.layers.BatchNormalization()(dense)
+    dense = tf.keras.layers.Dense(100, activation=tf.nn.softmax)(dense)
+    model = tf.keras.Model(inputs=inputs,outputs= dense)
+    return model
+
+
+if __name__ == '__main__':
+    # 全连接层大小和上面循环的层数有所减小，否则训练不动
+    # 报错tensorflow错误解决：“tensorflow.python.framework.errors_impl.ResourceExhaustedError”
+    #解决方法：
+    resnet_model = resnet_Model()
+    resnet_model.compile(optimizer=tf.optimizers.Adam(1e-2), loss=tf.losses.sparse_categorical_crossentropy,
+                         metrics=['accuracy'])
+    resnet_model.fit(train_data, train_label, epochs=10,batch_size=100)
+    score = resnet_model.evaluate(test_data, test_label)
+    print('score:', score)

TensorFlow_eaxmple/TensorFlow_constant_Variable/Fashion_MINST数据集实战.py

@@ -0,0 +1,65 @@
+import tensorflow as tf
+import numpy as np
+
+fashion_mnist_data = tf.keras.datasets.fashion_mnist
+(train_data, train_label), (test_data, test_label) = fashion_mnist_data.load_data()
+# 将数据修改为符合卷积模型输入条件的格式
+train_data = tf.expand_dims(train_data, axis=3)
+test_data = tf.expand_dims(test_data, axis=3)
+
+'''这里使用了正统的模型类定义方式，首先生成了一个 FashionClass类名，
+在init函数中对所有所需要用到的层进行定义，在_call_函数中对层进行了调用'''
+
+
+class FashionClass:
+    def __init__(self):
+        # 第一个卷积层和正则化层
+        self.conv1 = tf.keras.layers.Conv2D(32, 3, activation=tf.nn.relu)
+        self.bhn1 = tf.keras.layers.BatchNormalization()
+        # 第二个卷积层和正则化层
+        self.conv2 = tf.keras.layers.Conv2D(64, 3, activation=tf.nn.relu)
+        self.bhn2 = tf.keras.layers.BatchNormalization()
+        # 第三个卷积层和正则化层
+        self.conv3 = tf.keras.layers.Conv2D(128, 3, activation=tf.nn.relu)
+        self.bhn3 = tf.keras.layers.BatchNormalization()
+        # Maxpooling层
+        self.pooling = tf.keras.layers.MaxPool2D(strides=[1, 1])
+        # flatten层
+        self.flatten = tf.keras.layers.Flatten()
+        self.dropout = tf.keras.layers.Dropout(0.2)
+        # 全连接层
+        self.dense = tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+
+    def __call__(self, inputs):
+        img = inputs
+        img = self.conv1(inputs)
+        img = self.bhn1(img)
+        img = self.conv2(img)
+        img = self.bhn2(img)
+        img = self.conv3(img)
+        img = tf.nn.relu(img) + img
+        img = self.bhn3(img)
+        img = self.pooling(img)
+        img_flatten = self.flatten(img)
+        img_flatten = self.dropout(img_flatten)
+        img_dense = self.dense(img_flatten)
+
+        return img_dense
+
+
+if __name__ == '__main__':
+    inputs = tf.keras.Input(shape=(28, 28, 1))
+    outputs = FashionClass()(inputs)
+    model = tf.keras.Model(inputs, outputs)
+    model.compile(optimizer=tf.optimizers.Adam(1e-4), loss=tf.losses.sparse_categorical_crossentropy,
+                  metrics=['accuracy'])
+    # verbose=2设置了显示结果
+    model.fit(train_data, train_label, epochs=10,verbose=2)
+    model.evaluate(test_data, test_label)
+    print(model.summary())
+    # 图形化模型输入输出函数
+    tf.keras.utils.plot_model(model)
+    tensorboard = tf.keras.callbacks.TensorBoard(histogram_freq=1)
+
+    tf.keras.layers.Dense
+    map()

TensorFlow_eaxmple/TensorFlow_constant_Variable/HelloTensorflow.py

@@ -0,0 +1,32 @@
+import tensorflow as tf
+import numpy as np
+
+xs = np.random.normal(size=(10000, 3))
+weight = [[2.1], [1.7], [2.8]]
+bias = [3.3]
+#y=ax+b
+ys = np.matmul(xs, weight) + bias
+
+
+class Computer(tf.keras.layers.Layer):
+    def __init__(self):
+        super(Computer, self).__init__()
+        self.weight = tf.Variable(initial_value=(tf.random.truncated_normal(shape=[3, 1])), name="weight")
+        self.bias = tf.Variable(initial_value=(tf.random.truncated_normal(shape=[1])), name="bias")
+
+    #call()函数的基本用法就是让类可以变为一个函数去使用
+    def call(self, inputs):
+        token_num = inputs
+        output = tf.matmul(token_num, self.weight) + self.bias
+        return output
+
+
+if __name__ == "__main__":
+    input_xs = tf.keras.Input(shape=(3,))
+    output = Computer()(input_xs)
+    model = tf.keras.Model(input_xs, output)
+    model.compile(tf.keras.optimizers.SGD(1e-3), loss=tf.keras.losses.MSE, metrics=["MSE"])
+    model.fit(xs, ys, epochs=214)
+
+    print("拟合后的weight值为：", model.weights[0].numpy())
+    print("拟合后的bias值为：", model.weights[1].numpy())

TensorFlow_eaxmple/TensorFlow_constant_Variable/MINST数据集实战.py

@@ -0,0 +1,43 @@
+import tensorflow as tf
+import numpy as np
+
+# 载入数据
+data = tf.keras.datasets.mnist
+(train_x, train_y), (test_x, test_y) = data.load_data()
+print(train_x.shape,test_x.shape)
+# 数据处理
+train_x, test_x = train_x / 2511.0, test_x / 2511.0
+print(train_x.shape,test_x.shape)
+train_x = tf.expand_dims(train_x, -1)
+test_x = tf.expand_dims(test_x, -1)
+# 看看这个操作是在做什么：
+print(train_x.shape,test_x.shape)
+train_y = np.float32(tf.keras.utils.to_categorical(train_y, num_classes=10))
+test_y = np.float32(tf.keras.utils.to_categorical(test_y, num_classes=10))
+# 设置训练集,测试集大小
+batch_size = 512
+train_data = tf.data.Dataset.from_tensor_slices((train_x, train_y)).batch(batch_size).shuffle(batch_size * 10)
+test_data = tf.data.Dataset.from_tensor_slices((test_x, test_y)).batch(batch_size).shuffle(batch_size * 10)
+# 开始建模
+input_xs = tf.keras.Input(shape=(28, 28, 1))
+# 32维的[3,3]的卷积核
+conv = tf.keras.layers.Conv2D(32, 3, padding="SAME", activation=tf.nn.relu)(input_xs)
+# 正则化的工具，正则化输入数据，最大限度的减少模型的过拟合
+conv=tf.keras.layers.BatchNormalization()(conv)
+conv = tf.keras.layers.Conv2D(64, 3, padding="SAME", activation=tf.nn.relu)(conv)
+conv=tf.keras.layers.MaxPool2D(strides=[1,1])(conv)
+conv = tf.keras.layers.Conv2D(128, 3, padding="SAME", activation=tf.nn.relu)(conv)
+# 扁平化，扁平为1维数据，连入全连接层
+flatten=tf.keras.layers.Flatten()(conv)
+dense=tf.keras.layers.Dense(512,activation='relu')(flatten)
+# 分为十类
+logits=tf.keras.layers.Dense(10,activation='softmax')(dense)
+
+model=tf.keras.Model(inputs=input_xs,outputs=logits)
+
+model.compile(optimizer=tf.optimizers.Adam(1e-3),loss=tf.losses.categorical_crossentropy,metrics=['accuracy'])
+print(model.summary())
+model.fit(train_data,epochs=10)
+model.save('../saver/model.h5')
+score=model.evaluate(test_data)
+print("last score:",score)

TensorFlow_eaxmple/TensorFlow_constant_Variable/ResNet的CIFAR-100数据集实战.py

@@ -0,0 +1,170 @@
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import sys
+import loadData
+import labeled_and_piece
+import numpy as np
+
+# 导入数据，并进行数据处理
+# 导入第一类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'))
+dataWithLabel1 = labeled_and_piece.ConcatLabel(data, 1, False)
+dataWithLabel1 = np.array(dataWithLabel1(data, 1, False))
+
+# 导入第二类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
+dataWithLabel2 = labeled_and_piece.ConcatLabel(data, 2, False)
+dataWithLabel2 = np.array(dataWithLabel2(data, 2, False))
+
+# 导入第三类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA'))
+dataWithLabel3 = labeled_and_piece.ConcatLabel(data, 3, False)
+dataWithLabel3 = np.array(dataWithLabel3(data, 3, False))
+
+# 导入第四类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA'))
+dataWithLabel4 = labeled_and_piece.ConcatLabel(data, 4, False)
+dataWithLabel4 = np.array(dataWithLabel4(data, 4, False))
+
+# 导入第五类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA'))
+dataWithLabel5 = labeled_and_piece.ConcatLabel(data, 5, False)
+dataWithLabel5 = np.array(dataWithLabel5(data, 5, False))
+
+# 导入第六类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA'))
+dataWithLabel6 = labeled_and_piece.ConcatLabel(data, 6, False)
+dataWithLabel6 = np.array(dataWithLabel6(data, 6, False))
+
+# 导入第七类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA'))
+dataWithLabel7 = labeled_and_piece.ConcatLabel(data, 7, False)
+dataWithLabel7 = np.array(dataWithLabel7(data, 7, False))
+
+# 导入第八类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA'))
+dataWithLabel8 = labeled_and_piece.ConcatLabel(data, 8, False)
+dataWithLabel8 = np.array(dataWithLabel8(data, 8, False))
+
+# 导入第九类故障并打标签
+data0 = loadData.DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA')
+data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA'))
+dataWithLabel9 = labeled_and_piece.ConcatLabel(data, 9, False)
+dataWithLabel9 = np.array(dataWithLabel9(data, 9, False))
+
+data_all = tf.concat(
+    [dataWithLabel1, dataWithLabel2, dataWithLabel3, dataWithLabel4, dataWithLabel5, dataWithLabel6, dataWithLabel7,
+     dataWithLabel8, dataWithLabel9], axis=0)
+data_all = np.array(data_all)
+data_train = tf.random.shuffle(data_all)
+# print(data_train)
+data_new = labeled_and_piece.PieceAndBag(data_train, False)
+(train_data, train_label), (test_data, test_label) = data_new(data_train, False)
+train_data = np.array(train_data)
+train_label = np.array(train_label)
+test_data = np.array(test_data)
+test_label = np.array(test_label)
+
+'''print(train_data.shape,train_data.shape[0],train_data.shape[1])
+print(train_label.shape,train_label.shape[0])
+print(test_data.shape,test_data.shape[0],test_data.shape[1])
+print(test_label.shape,test_label.shape[0])'''
+
+train_data = tf.broadcast_to(train_data, [3, train_data.shape[0], train_data.shape[1]])
+train_label = tf.broadcast_to(train_label, [3, train_label.shape[0]])
+test_data = tf.broadcast_to(test_data, [3, test_data.shape[0], test_data.shape[1]])
+test_label = tf.broadcast_to(test_label, [3, test_label.shape[0]])
+
+train_data = tf.transpose(train_data, [1, 2, 0])
+train_label = tf.transpose(train_label, [1, 0])
+test_data = tf.transpose(test_data, [1, 2, 0])
+test_label = tf.transpose(test_label, [1, 0])
+
+print(train_data.shape)
+print(train_label.shape)
+print(test_data.shape)
+print(test_label.shape)
+'''
+train_data.shape:(2430, 204800)
+train_label.shape:(2430,)
+test_data.shape:(486, 204800)
+(test_label.shape:486,)'''
+
+'''CIFAR_100_data = tf.keras.datasets.cifar100
+(train_data, train_label), (test_data, test_label) = CIFAR_100_data.load_data()
+# print(train_data)
+# print(test_data)'''
+
+
+def identity_block(input_tensor, out_dim):
+    con1 = tf.keras.layers.Conv1D(filters=out_dim // 4, kernel_size=1, padding='SAME', activation=tf.nn.relu)(
+        input_tensor)
+    bhn1 = tf.keras.layers.BatchNormalization()(con1)
+
+    con2 = tf.keras.layers.Conv1D(filters=out_dim // 4, kernel_size=3, padding='SAME', activation=tf.nn.relu)(bhn1)
+    bhn2 = tf.keras.layers.BatchNormalization()(con2)
+
+    con3 = tf.keras.layers.Conv1D(filters=out_dim, kernel_size=1, padding='SAME', activation=tf.nn.relu)(bhn2)
+
+    out = tf.keras.layers.Add()([input_tensor, con3])
+    out = tf.nn.relu(out)
+
+    return out
+
+
+def resnet_Model():
+    inputs = tf.keras.Input(shape=[ 204800, 3])
+    conv1 = tf.keras.layers.Conv1D(filters=64, kernel_size=3, padding='SAME', activation=tf.nn.relu)(inputs)
+    '''第一层'''
+    output_dim = 64
+    identity_1 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        conv1)
+    identity_1 = tf.keras.layers.BatchNormalization()(identity_1)
+    for _ in range(1):
+        identity_1 = identity_block(identity_1, output_dim)
+    '''第二层'''
+    output_dim = 128
+    identity_2 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_1)
+    identity_2 = tf.keras.layers.BatchNormalization()(identity_2)
+    for _ in range(1):
+        identity_2 = identity_block(identity_2, output_dim)
+    '''第三层'''
+    output_dim = 256
+    identity_3 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_2)
+    identity_3 = tf.keras.layers.BatchNormalization()(identity_3)
+    for _ in range(1):
+        identity_3 = identity_block(identity_3, output_dim)
+    '''第四层'''
+    output_dim = 512
+    identity_4 = tf.keras.layers.Conv1D(filters=output_dim, kernel_size=3, padding='SAME', activation=tf.nn.relu)(
+        identity_3)
+    identity_4 = tf.keras.layers.BatchNormalization()(identity_4)
+    for _ in range(1):
+        identity_4 = identity_block(identity_4, output_dim)
+    flatten = tf.keras.layers.Flatten()(identity_4)
+    dropout = tf.keras.layers.Dropout(0.217)(flatten)
+    '''dense = tf.keras.layers.Dense(1, activation=tf.nn.relu)(dropout)
+    dense = tf.keras.layers.BatchNormalization()(dense)'''
+    dense = tf.keras.layers.Dense(9, activation=tf.nn.softmax)(dropout)
+    model = tf.keras.Model(inputs=inputs, outputs=dense)
+    return model
+
+
+if __name__ == '__main__':
+    resnet_model = resnet_Model()
+    resnet_model.compile(optimizer=tf.optimizers.Adam(1e-2), loss=tf.losses.sparse_categorical_crossentropy,
+                         metrics=['accuracy'])
+    resnet_model.summary()
+    resnet_model.fit(train_data, train_label, epochs=10, batch_size=10)
+    score = resnet_model.evaluate(test_data, test_label)
+    print('score:', score)

TensorFlow_eaxmple/TensorFlow_constant_Variable/datasets装载的数据集.py

@@ -0,0 +1,15 @@
+import tensorflow_datasets as tfds
+splits=tfds.Split.TRAIN.subsplit(weighted=[2,1,1])
+# tfds.Split.TRAIN.subsplit函数按传入的权重将其分成为训练集占50%，验证集占25%，测试集占25%
+# metadata属性用于获取MNIST数据集的基本信息，包括数据的种类，大小以及对应的形式
+(raw_trian,raw_validation,raw_test),metadata=tfds.load('mnist',split=list(splits),with_info=True,as_supervised=True)
+import tensorflow as tf
+# load函数中添加split参数，表示将数据在传入的时候直接进行分割，按数据的类型分割成“image”和“model”
+# batch_size=-1,可以从返回的tf.Tensor对象获取NumPy数组中完整数据集
+minst_data=tfds.load('mnist',split=tfds.Split.TRAIN,batch_size=-1)
+numpy_ds=tfds.as_numpy(minst_data)
+numpy_image,numpy_label=numpy_ds["image"],numpy_ds["label"]
+'''minst_train,minst_test=minst_data['train'],minst_data['test']
+
+print(minst_train)
+print(minst_test'''

TensorFlow_eaxmple/TensorFlow_constant_Variable/labeled_and_piece.py

@@ -0,0 +1,97 @@
+import loadData
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+
+'''
+class ConcatLabel：
+这是一个给data加上label的类
+使用示例：ConcatLabel(data, label,if_load)
+作用是按是否采用加载将data文件分别添加上label
+需要传入的参数有：
+data:要添加label的data  是一个list类型或者np.array
+label:添加的label为几   最好是int类型
+if_load:是否采用按加载来分  true则采用，false则不采用   布尔类型
+'''
+
+
+class ConcatLabel(object):
+
+    def __init__(self, data, label, if_load):
+        self.data = data
+        self.if_load = if_load
+        self.label = label
+
+    def __call__(self, *args, **kwargs):
+        data = np.array(self.data)
+        if not self.if_load:
+            length, dim = data.shape
+            label_list = [[self.label] for i in range(length)]
+            label_list = np.array(label_list)
+            dataWithLabel = tf.concat([data, label_list], axis=1)
+            return dataWithLabel
+        height, length, dim = data.shape
+        data_new = tf.reshape(data, [height * length, dim])
+        label_list = [[self.label] for i in range(height * length)]
+        label_list = np.array(label_list)
+        dataWithLabel = tf.concat([data_new, label_list], axis=1)
+        dataWithLabel = tf.reshape(dataWithLabel, [height, length, dim + 1])
+        return dataWithLabel
+
+
+'''
+class PieceAndBag：
+这是一个给data分成train_data和test_data的类
+使用示例：ConcatLabel(data, if_load)
+作用是按是否采用加载将data文件分成(train_data,train_label和(test_data,test_label)
+需要传入的参数有：
+data:要添加label的data  是一个list类型或者np.array
+if_load:是否采用按加载来分  true则采用，false则不采用   布尔类型
+'''
+
+
+class PieceAndBag(object):
+
+    def __init__(self, data, if_load):
+        self.data = data
+        self.if_load = if_load
+
+    def __call__(self, *args, **kwargs):
+        data = self.data
+        data = np.array(data)
+        test_data = []
+        train_data = []
+        test_label = []
+        train_label = []
+        if not self.if_load:
+            heigth, dim = data.shape
+            for i in range(heigth):
+                if i % 6 == 0:
+                    test_data.append(data[i, :-1])
+                    test_label.append(data[i, -1])
+                else:
+                    train_data.append(data[i, :-1])
+                    train_label.append(data[i, -1])
+            train_data=np.array(train_data)
+            train_label = np.array(train_label)
+            test_data = np.array(test_data)
+            test_label = np.array(test_label)
+            return (train_data,train_label),(test_data,test_label)
+        height, length, dim = data.shape
+        data = tf.reshape(data, [height * length, dim])
+        for i in range(height * length):
+            if i % 6 == 0:
+                test_data.append(data[i, :-1])
+                test_label.append(data[i, -1])
+            else:
+                train_data.append(data[i, :-1])
+                train_label.append(data[i, -1])
+        train_data = tf.reshape(np.array(train_data), [height, length, dim - 1])
+        train_label = tf.reshape(np.array(train_label), [height, length, 1])
+        test_data = tf.reshape(np.array(test_data), [height, length, dim - 1])
+        test_label = tf.reshape(np.array(test_label), [height, length, 1])
+        train_data=tf.transpose(train_data,[1,2,0])
+        train_label = tf.transpose(train_label, [1, 2, 0])
+        test_data = tf.transpose(test_data, [1, 2, 0])
+        test_label = tf.transpose(test_label, [1, 2, 0])
+        return (train_data, train_label), (test_data, test_label)

TensorFlow_eaxmple/TensorFlow_constant_Variable/loadData.py

@@ -0,0 +1,139 @@
+import numpy as np
+import os
+import tensorflow as tf
+
+'''
+class DataDeal1：
+这是一个处理data的类
+使用示例：DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+作用是将后缀为.dat的文件按节点个数(head)和加载的点数n的不同解析为(3,108,dim/108)维的文件
+需要传入的参数有：
+head:节点头数   int类型
+filename:放置.dat文件的根路径   string类型
+'''
+
+
+class DataDeal1(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        result = [[[] * self.head] * k.shape[0]]
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum0 = [[] for i in range(self.head)]
+        sum1 = [[] for i in range(self.head)]
+        sum2 = [[] for i in range(self.head)]
+        '''sum0=[None]*self.head
+        sum1 = [None] * self.head
+        sum2 = [None] * self.head
+        for i in range(self.head):
+            sum0[i] = [0]* 204800
+            sum1[i] = [0]* 204800
+            sum2[i] = [0]* 204800'''
+        z = 0
+        for filename in Z:
+            # 将二进制文件读成np数组
+            each_data = np.fromfile(filename, dtype=np.float32)
+            data = np.array(each_data)
+            # 创建一个二维空数组
+            data_true = [[] for i in range(self.head)]
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            for i in range(self.head):
+                data_true[i] = data[i::self.head]
+                # 如果第一次是空数剧，则无法进行拼接
+                if z == 0:
+                    sum0[i] = data_true[i]
+                if z == 4:
+                    sum1[i] = data_true[i]
+                if z == 8:
+                    sum2[i] = data_true[i]
+            # 将二维空数组重新整合成np.array的形式
+            data_true = np.array(data_true)
+            if z % 12 == 0 or z % 12 == 1 or z % 12 == 2 or z % 12 == 3:
+                if z != 0:
+                    sum0 = np.vstack((sum0, data_true))
+            if z % 12 == 4 or z % 12 == 6 or z % 12 == 7 or z % 12 == 5:
+                if z != 4:
+                    sum1 = np.vstack((sum1, data_true))
+            if z % 12 == 9 or z % 12 == 10 or z % 12 == 11 or z % 12 == 8:
+                if z != 8:
+                    sum2 = np.vstack((sum2, data_true))
+            z = z + 1
+        sum0 = np.array(sum0)
+        sum1 = np.array(sum1)
+        sum2 = np.array(sum2)
+        print("sum0:", sum0.shape)
+        print("sum1:", sum1.shape)
+        print("sum2:", sum2.shape)
+        return sum0, sum1, sum2
+
+
+'''
+class DataDeal2：
+这是一个处理data的类
+使用示例：DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+作用是将后缀为.dat的文件按节点个数(head)解析为(3*108,dim/108)维的文件
+需要传入的参数有：
+head:节点头数   int类型
+filename:放置.dat文件的根路径   string类型
+'''
+
+
+class DataDeal2(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum = [[] for i in range(self.head)]
+        z = 0
+        for filename in Z:
+            # 将二进制文件读成np数组
+            each_data = np.fromfile(filename, dtype=np.float32)
+            data = np.array(each_data)
+            # 创建一个二维空数组
+            data_true = [[] for i in range(self.head)]
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            for i in range(self.head):
+                data_true[i] = data[i::self.head]
+                # 如果第一次是空数剧，则无法进行拼接
+                if z == 0:
+                    sum[i] = data_true[i]
+            # 将二维空数组重新整合成np.array的形式
+            data_true = np.array(data_true)
+            if z != 0:
+                sum = np.vstack((sum, data_true))
+            z = z + 1
+        sum = np.array(sum)
+        print("sum:", sum.shape)
+        return sum

TensorFlow_eaxmple/TensorFlow_constant_Variable/review.py

@@ -0,0 +1,26 @@
+import tensorflow as tf
+import numpy as np
+from sklearn.datasets import load_iris
+data = load_iris()
+iris_target = data.target
+iris_data = np.float32(data.data)
+print('first:',iris_data.shape)
+iris_target = np.float32(tf.keras.utils.to_categorical(iris_target,num_classes=3))
+iris_data = tf.data.Dataset.from_tensor_slices(iris_data).batch(50)
+print('first:',iris_data.shape)
+iris_target = tf.data.Dataset.from_tensor_slices(iris_target).batch(50)
+model = tf.keras.models.Sequential()
+# Add layers
+model.add(tf.keras.layers.Dense(32, activation="relu"))
+model.add(tf.keras.layers.Dense(64, activation="relu"))
+model.add(tf.keras.layers.Dense(3,activation="softmax"))
+opt = tf.optimizers.Adam(1e-3)
+for epoch in range(1000):
+    for _data,lable in zip(iris_data,iris_target):
+        with tf.GradientTape() as tape:
+            '''固定的写法，tf.reduce_mean计算损失函数，tape.gradient计算gradient'''
+            logits = model(_data)
+            loss_value = tf.reduce_mean(tf.keras.losses.categorical_crossentropy(y_true = lable,y_pred = logits))
+            grads = tape.gradient(loss_value, model.trainable_variables)
+            opt.apply_gradients(zip(grads, model.trainable_variables))
+    print('Training loss is :', loss_value.numpy())

TensorFlow_eaxmple/TensorFlow_constant_Variable/tensorflow常量、变量和数据类型.py

@@ -0,0 +1,7 @@
+import tensorflow as tf
+
+a1=tf.constant(1,dtype=tf.float32)
+a2=tf.Variable(1)
+
+print(a1)
+print(a2)

TensorFlow_eaxmple/TensorFlow_constant_Variable/卷积函数实现详解.py

@@ -0,0 +1,11 @@
+import tensorflow as tf
+
+matric1=tf.random.normal([1,5,5,1])
+input=tf.Variable(matric1)
+print('matric1',matric1)
+conv=tf.keras.layers.Conv2D(1,2,padding="SAME")(input)
+#上式等价于conv=tf.keras.layers.Conv2D(filters=1,kernel_size=2)(input)
+print('conv的大小：',conv.shape)
+
+#tf.keras.layers.Conv2D()
+tf.keras.layers.MaxPool2D

TensorFlow_eaxmple/TensorFlow_constant_Variable/多输入单输出.py

@@ -0,0 +1,27 @@
+import tensorflow as tf
+import numpy as np
+from sklearn.datasets import load_iris
+
+data = load_iris()
+iris_data = np.float32(data.data)
+iris_data_1 = []
+iris_data_2 = []
+for iris in iris_data:
+    iris_data_1.append(iris[0])
+    iris_data_2.append(iris[1:4])
+iris_target = np.float32(tf.keras.utils.to_categorical(data.target,num_classes=3))
+#注意数据的包裹层数。
+train_data = tf.data.Dataset.from_tensor_slices(((iris_data_1,iris_data_2),iris_target)).batch(128)
+
+input_xs_1  = tf.keras.Input(shape=(1,), name='input_xs_1')       #接收输入参数一
+input_xs_2  = tf.keras.Input(shape=(3,), name='input_xs_2')       #接收输入参数二
+input_xs = tf.concat([input_xs_1,input_xs_2],axis=-1)        #重新组合参数
+out = tf.keras.layers.Dense(32, activation='relu', name='dense_1')(input_xs)
+out = tf.keras.layers.Dense(64, activation='relu', name='dense_2')(out)
+logits = tf.keras.layers.Dense(3, activation="softmax",name='predictions')(out)
+model = tf.keras.Model(inputs=[input_xs_1,input_xs_2], outputs=logits)  #请注意model中的中括号
+opt = tf.optimizers.Adam(1e-3)
+model.compile(optimizer=tf.optimizers.Adam(1e-3), loss=tf.losses.categorical_crossentropy,metrics = ['accuracy'])
+model.fit(x = train_data, epochs=500)
+score = model.evaluate(train_data)
+print("多头score：",score)

TensorFlow_eaxmple/TensorFlow_constant_Variable/多输入多输出.py

@@ -0,0 +1,36 @@
+import tensorflow as tf
+import numpy as np
+from sklearn.datasets import load_iris
+
+data = load_iris()
+iris_data = np.float32(data.data)
+iris_data_1 = []
+iris_data_2 = []
+for iris in iris_data:
+    iris_data_1.append(iris[:2])
+    iris_data_2.append(iris[2:])
+iris_label = np.float32(data.target)
+iris_target = np.float32(tf.keras.utils.to_categorical(data.target, num_classes=3))
+train_data = tf.data.Dataset.from_tensor_slices(((iris_data_1, iris_data_2), (iris_target, iris_label))).batch(128)
+input_xs_1 = tf.keras.Input(shape=(2,), name='input_xs_1')
+input_xs_2 = tf.keras.Input(shape=(2,), name='input_xs_2')
+input_xs = tf.concat([input_xs_1, input_xs_2], axis=-1)
+out = tf.keras.layers.Dense(32, activation='relu', name='dense_1')(input_xs)
+out = tf.keras.layers.Dense(64, activation='relu', name='dense_2')(out)
+logits = tf.keras.layers.Dense(3, activation="softmax", name='predictions')(out)
+label = tf.keras.layers.Dense(1, name='label')(out)
+model = tf.keras.Model(inputs=[input_xs_1, input_xs_2], outputs=[logits, label])
+opt = tf.optimizers.Adam(1e-3)
+
+
+def my_MSE(y_true, y_pred):
+    my_loss = tf.reduce_mean(tf.square(y_true - y_pred))
+    return my_loss
+
+
+model.compile(optimizer=tf.optimizers.Adam(1e-3),
+              loss={'predictions': tf.losses.categorical_crossentropy, 'label': my_MSE},
+              loss_weights={'predictions': 0.1, 'label': 0.5}, metrics=['accuracy'])
+model.fit(x=train_data, epochs=500)
+score = model.evaluate(train_data)
+print("多头score：", score)

TensorFlow_eaxmple/TensorFlow_constant_Variable/最小二乘法.py

@@ -0,0 +1,35 @@
+import numpy as np
+from matplotlib import pyplot as plt
+
+A = np.array([[5], [4]])
+C = np.array([[4], [6]])
+B = A.T.dot(C)
+AA = np.linalg.inv(A.T.dot(A))
+l = AA.dot(B)
+P = A.dot(l)
+x = np.linspace(-2, 2, 10)
+x.shape = (1, 10)
+xx = A.dot(x)
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(xx[0, :], xx[1, :])
+ax.plot(A[0], A[1], 'ko')
+
+ax.plot([C[0], P[0]], [C[1], P[1]], 'r-o')
+ax.plot([0, C[0]], [0, C[1]], 'm-o')
+
+ax.axvline(x=0, color='black')
+ax.axhline(y=0, color='black')
+
+margin = 0.1
+ax.text(A[0] + margin, A[1] + margin, r"A", fontsize=20)
+ax.text(C[0] + margin, C[1] + margin, r"C", fontsize=20)
+ax.text(P[0] + margin, P[1] + margin, r"P", fontsize=20)
+ax.text(0 + margin, 0 + margin, r"O", fontsize=20)
+ax.text(0 + margin, 4 + margin, r"y", fontsize=20)
+ax.text(4 + margin, 0 + margin, r"x", fontsize=20)
+plt.xticks(np.arange(-2, 3))
+plt.yticks(np.arange(-2, 3))
+
+ax.axis('equal')
+plt.show()

TensorFlow_eaxmple/TensorFlow_constant_Variable/矩阵计算.py

@@ -0,0 +1,11 @@
+import tensorflow as tf
+
+# 使用tf.constant来创建一个张量矩阵
+a1 = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
+print(a1)
+print(tf.shape(a1))
+tf.random.random_normal(shape,mean=0.0)
+tf.random_normal_initializer(shape,mean=0.0,stddev=1.0,dtype=tf.float32,seed=None,name=None)
+tf.random_uniform_initializer(shape,minval=0,maxval=None,dtype=tf.float32,seed=None,name=None)
+tf.truncated_random_normal_initializer(shape,mean=0.0,stddev=1.0,dtype=tf.float32,seed=None,name=None)
+

TensorFlow_eaxmple/TensorFlow_constant_Variable/自定义层打印结构.py

@@ -0,0 +1,36 @@
+import tensorflow as tf
+import numpy as np
+from sklearn.datasets import load_iris
+
+data = load_iris()
+iris_data = np.float32(data.data)
+iris_target = np.float32(data.target)
+iris_target = np.float32(tf.keras.utils.to_categorical(iris_target, num_classes=3))
+train_data = tf.data.Dataset.from_tensor_slices((iris_data, iris_target)).batch(128)
+
+
+class MyLayer(tf.keras.layers.Layer):
+    def __init__(self, output_dim):
+        self.output_dim = output_dim
+        super(MyLayer, self).__init__()
+
+    def build(self, input_shape):
+        self.weight = tf.Variable(tf.random.normal([input_shape[-1], self.output_dim]), name="dense_weight")
+        self.bias = tf.Variable(tf.random.normal([self.output_dim]), name="bias_weight")
+        super(MyLayer, self).build(input_shape)  # Be sure to call this somewhere!
+
+    def call(self, input_tensor):
+        out = tf.matmul(input_tensor, self.weight) + self.bias
+        out = tf.nn.relu(out)
+        out = tf.keras.layers.Dropout(0.1)(out)
+        return out
+
+
+input_xs = tf.keras.Input(shape=(4), name='input_xs')
+out = tf.keras.layers.Dense(32, activation='relu', name='dense_1')(input_xs)
+out = MyLayer(32)(out)
+out = MyLayer(48)(out)
+out = tf.keras.layers.Dense(64, activation='relu', name='dense_2')(out)
+logits = tf.keras.layers.Dense(3, activation="softmax", name='predictions')(out)
+model = tf.keras.Model(inputs=input_xs, outputs=logits)
+print(model.summary())

TensorFlow_eaxmple/TensorFlow_constant_Variable/自定义层残差网络的MINST识别.py

@@ -0,0 +1,65 @@
+import tensorflow as tf
+import numpy as np
+
+# 载入数据
+data = tf.keras.datasets.mnist
+(train_x, train_y), (test_x, test_y) = data.load_data()
+print(train_x.shape, test_x.shape)
+# 数据处理
+train_x, test_x = train_x / 2511.0, test_x / 2511.0
+print(train_x.shape, test_x.shape)
+train_x = tf.expand_dims(train_x, -1)
+test_x = tf.expand_dims(test_x, -1)
+# 看看这个操作是在做什么：
+print(train_x.shape, test_x.shape)
+train_y = np.float32(tf.keras.utils.to_categorical(train_y, num_classes=10))
+test_y = np.float32(tf.keras.utils.to_categorical(test_y, num_classes=10))
+# 设置训练集,测试集大小
+batch_size = 512
+train_data = tf.data.Dataset.from_tensor_slices((train_x, train_y)).batch(batch_size).shuffle(batch_size * 10)
+test_data = tf.data.Dataset.from_tensor_slices((test_x, test_y)).batch(batch_size).shuffle(batch_size * 10)
+
+
+# 自定义层实现残差网络
+class MyLayer(tf.keras.layers.Layer):
+    def __init__(self, kernel_size, filter):
+        self.kernel_size = kernel_size
+        self.filter = filter
+        super(MyLayer, self).__init__()
+
+    def build(self, input_shape):
+        self.weight = tf.Variable(tf.random.normal([self.kernel_size, self.kernel_size, input_shape[-1], self.filter]))
+        self.bias = tf.Variable(tf.random.normal([self.filter]))
+        super(MyLayer, self).build(input_shape)
+
+    def call(self, input_tensor):
+        conv = tf.nn.conv2d(input_tensor, self.weight, strides=[1, 2, 2, 1], padding='SAME')
+        conv = tf.nn.bias_add(conv, self.bias)
+        out = tf.nn.relu(conv) + conv
+        return out
+
+
+# 开始建模
+input_xs = tf.keras.Input(shape=(28, 28, 1))
+# 32维的[3,3]的卷积核
+#conv = tf.keras.layers.Conv2D(32, 3, padding="SAME", activation=tf.nn.relu)(input_xs)
+conv = MyLayer(32, 3)(input_xs)
+# 正则化的工具，正则化输入数据，最大限度的减少模型的过拟合
+conv = tf.keras.layers.BatchNormalization()(conv)
+conv = tf.keras.layers.Conv2D(64, 3, padding="SAME", activation=tf.nn.relu)(conv)
+conv = tf.keras.layers.MaxPool2D(strides=[1, 1])(conv)
+conv = tf.keras.layers.Conv2D(128, 3, padding="SAME", activation=tf.nn.relu)(conv)
+# 扁平化，扁平为1维数据，连入全连接层
+flatten = tf.keras.layers.Flatten()(conv)
+dense = tf.keras.layers.Dense(512, activation='relu')(flatten)
+# 分为十类
+logits = tf.keras.layers.Dense(10, activation='softmax')(dense)
+
+model = tf.keras.Model(inputs=input_xs, outputs=logits)
+
+model.compile(optimizer=tf.optimizers.Adam(1e-3), loss=tf.losses.categorical_crossentropy, metrics=['accuracy'])
+print(model.summary())
+model.fit(train_data, epochs=10)
+#model.save('../saver/model.h5')
+score = model.evaluate(test_data)
+print("last score:", score)

TensorFlow_eaxmple/TensorFlow_constant_Variable/鸢尾花分类的Keras函数式写法.py

@@ -0,0 +1,30 @@
+import tensorflow as tf
+import numpy as np
+from sklearn.datasets import load_iris
+import cv2
+data = load_iris()
+iris_target = data.target
+iris_data = np.float32(data.data)
+iris_target = np.float32(tf.keras.utils.to_categorical(iris_target,num_classes=3))
+print(iris_target)
+iris_data = tf.data.Dataset.from_tensor_slices(iris_data).batch(50)
+iris_target = tf.data.Dataset.from_tensor_slices(iris_target).batch(50)
+inputs = tf.keras.layers.Input(shape=(4))
+# 层的实例是可调用的，它以张量为参数，并且返回一个张量
+x = tf.keras.layers.Dense(32, activation='relu')(inputs)
+x = tf.keras.layers.Dense(64, activation='relu')(x)
+predictions = tf.keras.layers.Dense(3, activation='softmax')(x)
+# 这部分创建了一个包含输入层和三个全连接层的模型
+model = tf.keras.Model(inputs=inputs, outputs=predictions)
+opt = tf.optimizers.Adam(1e-3)
+for epoch in range(1000):
+    for _data,lable in zip(iris_data,iris_target):
+        with tf.GradientTape() as tape:
+            logits = model(_data)
+            loss_value = tf.reduce_mean(tf.keras.losses.categorical_crossentropy(y_true = lable,y_pred = logits))
+            grads = tape.gradient(loss_value, model.trainable_variables)
+            opt.apply_gradients(zip(grads, model.trainable_variables))
+print('Training loss is :', loss_value.numpy())
+model.save('./saver/the_save_model.h5')
+
+

TensorFlow_eaxmple/TensorFlow_constant_Variable/鸢尾花分类的梯度更新函数式写法.py

@@ -0,0 +1,26 @@
+import tensorflow as tf
+import numpy as np
+from sklearn.datasets import load_iris
+data = load_iris()
+iris_target = data.target
+iris_data = np.float32(data.data)
+iris_target = np.float32(tf.keras.utils.to_categorical(iris_target,num_classes=3))
+iris_data = tf.data.Dataset.from_tensor_slices(iris_data).batch(50)
+iris_target = tf.data.Dataset.from_tensor_slices(iris_target).batch(50)
+model = tf.keras.models.Sequential()
+# Add layers
+model.add(tf.keras.layers.Dense(32, activation="relu"))
+model.add(tf.keras.layers.Dense(64, activation="relu"))
+model.add(tf.keras.layers.Dense(3,activation="softmax"))
+opt = tf.optimizers.Adam(1e-3)
+for epoch in range(1000):
+    for _data,lable in zip(iris_data,iris_target):
+        with tf.GradientTape() as tape:
+            '''固定的写法，tf.reduce_mean计算损失函数，tape.gradient计算gradient'''
+            logits = model(_data)
+            loss_value = tf.reduce_mean(tf.keras.losses.categorical_crossentropy(y_true = lable,y_pred = logits))
+            grads = tape.gradient(loss_value, model.trainable_variables)
+            opt.apply_gradients(zip(grads, model.trainable_variables))
+    print('Training loss is :', loss_value.numpy())
+
+

TensorFlow_eaxmple/data_deal/四分位图.py

@@ -0,0 +1,15 @@
+from pylab import *
+import pandas as pd
+import matplotlib.pyplot as plot
+
+datafile = pd.read_csv('E:\TensorFlow_eaxmple\dataset\某地降雨量.csv')
+'''计算求和平均等数据属性'''
+summary=datafile.describe()
+print(summary)
+
+array=datafile.iloc[:,1:13].values
+boxplot(array)
+print(array)
+plot.xlabel("month")
+plot.ylabel("rain")
+show()

TensorFlow_eaxmple/data_deal/平行化展示趋势图.py

@@ -0,0 +1,19 @@
+from pylab import *
+import pandas as pd
+import matplotlib.pyplot as plot
+
+datafile = pd.read_csv('E:\TensorFlow_eaxmple\dataset\某地降雨量.csv')
+print(datafile)
+'''计算求和平均等数据属性'''
+summary=datafile.describe()
+minRing=-1
+maxRing=99
+nrows=11
+for i in range(nrows):
+    '''每行数据用不同的颜色的线展示，每行数据即每年在十二月这十二个维度的数据，该方法即可平行化展示每年的这12个维度'''
+    dataRow=datafile.iloc[i,1:13]
+    labelColor=(datafile.iloc[i,12]-minRing)/(maxRing-minRing)
+    dataRow.plot(color=plot.cm.RdYlBu(labelColor),alpha=0.5)
+plot.xlabel("Attribute")
+plot.ylabel("Score")
+show()

TensorFlow_eaxmple/data_deal/热点图.py

@@ -0,0 +1,13 @@
+from pylab import *
+import pandas as pd
+import matplotlib.pyplot as plot
+
+datafile = pd.read_csv('E:\TensorFlow_eaxmple\dataset\某地降雨量.csv')
+print(datafile)
+'''计算求和平均等数据属性'''
+summary=datafile.describe()
+'''尚且存在问题，NameError: name 'DataFrame' is not defined'''
+corMat = pd.DataFrame(datafile.iloc[1:20,1:20].corr())
+print(corMat)
+plot.pcolor(corMat)
+plot.show()

TensorFlow_eaxmple/datadeal_try/data_deal.py

@@ -0,0 +1,112 @@
+from matplotlib import numpy
+import matplotlib.pyplot as plot
+import numpy as np
+import tensorflow as tf
+import os
+
+'''
+这是一个处理data的类
+作用是将后缀为.dat的文件按节点个数(head)解析为可用的十进制文件
+需要传入的参数有：
+head:节点头数   int类型
+filename:放置.dat文件的根路径   string类型
+'''
+
+
+class DataDeal(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        result = [[[] * self.head] * k.shape[0]]
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum0 = [[] for i in range(self.head)]
+        sum1 = [[] for i in range(self.head)]
+        sum2 = [[] for i in range(self.head)]
+        for filename in Z:
+            with open(filename, 'rb') as f:
+                # 解析为16进制数据
+                bs = ['{:0>2X}'.format(x) for x in f.read()]
+                # 将每一个数据分别转化为10进制数据，并存入voiceArray
+                for x in bs:
+                    # print(x)
+                    x = int(x, 16)
+                    self.voiceArray.append(x)
+
+            # 将voiceArray转化为np的数组
+            data = np.array(self.voiceArray)
+            print(data.shape)
+            # 创建一个二维空数组
+            data_true = [[] for i in range(self.head)]
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            for i in range(self.head):
+                data_true[i] = data[i::self.head]
+            # 将二维空数组重新整合成np.array的形式
+            data_true = np.array(data_true)
+
+            z = 0
+            if z % 12 == 0 or z % 12 == 1 or z % 12 == 2 or z % 12 == 3:
+                sum0 = np.hstack((sum0, data_true))
+            if z % 12 == 4 or z % 12 == 6 or z % 12 == 7 or z % 12 == 5:
+                sum1 = np.hstack((sum1, data_true))
+            if z % 12 == 9 or z % 12 == 10 or z % 12 == 11 or z % 12 == 8:
+                sum2 = np.hstack((sum2, data_true))
+            z=z+1
+        #sum0 = np.array(sum0)
+        #sum1 = np.array(sum1)
+        #sum2 = np.array(sum2)
+        print(sum0)
+        print(sum1)
+        print(sum2)
+        return sum0, sum1, sum2
+
+
+if __name__ == '__main__':
+    data0= DataDeal(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+    print(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
+'''    data0 = np.array(data0)
+    data1 = np.array(data1)
+    data2 = np.array(data2)
+    print(data0.shape)
+    print(data1.shape)
+    print(data2.shape)'''
+
+'''
+    # 读取文件
+    filename = r'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA\dds测试故障库4.6#0001.dat'
+    with open(filename, 'rb') as f:
+        # 解析为16进制数据
+        bs = ['{:0>2X}'.format(x) for x in f.read()]
+        # 将每一个数据分别转化为10进制数据，并存入voiceArray
+        for x in bs:
+            # print(x)
+            x = int(x, 16)
+            voiceArray.append(x)
+
+    # 将voiceArray转化为np的数组
+    data = np.array(voiceArray)
+    print(data.shape)
+    # 创建一个二维空数组
+    data_true = [[] for i in range(9)]
+    # 数据按节点个数分成head个分别存入二维空数组的各行
+    for i in range(9):
+        data_true[i] = data[i::9]
+    # 将二维空数组重新整合成np.array的形式
+    data_true = np.array(data_true)
+    print(data_true)
+'''

TensorFlow_eaxmple/datadeal_try/labeled_and_piece.py

@@ -0,0 +1,185 @@
+import loadData
+import numpy as np
+import tensorflow as tf
+import pandas as pd
+
+'''
+class ConcatLabel：
+这是一个给data加上label的类
+使用示例：ConcatLabel(data, label,if_load)
+作用是按是否采用加载将data文件分别添加上label
+需要传入的参数有：
+data:要添加label的data  是一个list类型或者np.array
+label:添加的label为几   最好是int类型
+if_load:是否采用按加载来分  true则采用，false则不采用   布尔类型
+'''
+
+
+class ConcatLabel(object):
+
+    def __init__(self, data, label, if_load):
+        self.data = data
+        self.if_load = if_load
+        self.label = label
+
+    def __call__(self, *args, **kwargs):
+        data = np.array(self.data)
+        if not self.if_load:
+            length, dim = data.shape
+            label_list = [[self.label] for i in range(length)]
+            label_list = np.array(label_list)
+            dataWithLabel = tf.concat([data, label_list], axis=1)
+            return dataWithLabel
+        height, length, dim = data.shape
+        data_new = tf.reshape(data, [height * length, dim])
+        label_list = [[self.label] for i in range(height * length)]
+        label_list = np.array(label_list)
+        dataWithLabel = tf.concat([data_new, label_list], axis=1)
+        dataWithLabel = tf.reshape(dataWithLabel, [height, length, dim + 1])
+        return dataWithLabel
+
+
+'''
+class PieceAndBag：
+这是一个给data分成train_data和test_data的类
+使用示例：ConcatLabel(data, if_load)
+作用是按是否采用加载将data文件分成(train_data,train_label和(test_data,test_label)
+需要传入的参数有：
+data:要添加label的data  是一个list类型或者np.array
+if_load:是否采用按加载来分  true则采用，false则不采用   布尔类型
+'''
+
+
+class PieceAndBag(object):
+
+    def __init__(self, data, if_load):
+        self.data = data
+        self.if_load = if_load
+
+    def __call__(self, *args, **kwargs):
+        data = self.data
+        data = np.array(data)
+        test_data = []
+        train_data = []
+        test_label = []
+        train_label = []
+        if not self.if_load:
+            heigth, dim = data.shape
+            for i in range(heigth):
+                if i % 6 == 0:
+                    test_data.append(data[i, :-1])
+                    test_label.append(data[i, -1])
+                else:
+                    train_data.append(data[i, :-1])
+                    train_label.append(data[i, -1])
+            train_data = np.array(train_data)
+            train_label = np.array(train_label)
+            test_data = np.array(test_data)
+            test_label = np.array(test_label)
+            return (train_data, train_label), (test_data, test_label)
+        height, length, dim = data.shape
+        data = tf.reshape(data, [height * length, dim])
+        for i in range(height * length):
+            if i % 6 == 0:
+                test_data.append(data[i, :-1])
+                test_label.append(data[i, -1])
+            else:
+                train_data.append(data[i, :-1])
+                train_label.append(data[i, -1])
+
+        train_data = np.array(train_data)
+        train_label = np.array(train_label)
+        test_data = np.array(test_data)
+        test_label = np.array(test_label)
+        print(train_data.shape)
+        print(train_label.shape)
+        print(test_data.shape)
+        print(test_label.shape)
+        train_data = tf.reshape(np.array(train_data), [height, length, dim - 1])
+        train_label = tf.reshape(np.array(train_label), [height, length, 1])
+        test_data = tf.reshape(np.array(test_data), [height, length, dim - 1])
+        test_label = tf.reshape(np.array(test_label), [height, length, 1])
+
+        train_data = tf.transpose(train_data, [1, 2, 0])
+        train_label = tf.transpose(train_label, [1, 2, 0])
+        test_data = tf.transpose(test_data, [1, 2, 0])
+        test_label = tf.transpose(test_label, [1, 2, 0])
+        return (train_data, train_label), (test_data, test_label)
+
+
+if __name__ == '__main__':
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'))
+    dataWithLabel1 = ConcatLabel(data, 1, True)
+    dataWithLabel1 = np.array(dataWithLabel1(data, 1, True))
+
+    # 导入第二类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
+    dataWithLabel2 = ConcatLabel(data, 2, True)
+    dataWithLabel2 = np.array(dataWithLabel2(data, 2, True))
+
+    # 导入第三类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA'))
+    dataWithLabel3 = ConcatLabel(data, 3, True)
+    dataWithLabel3 = np.array(dataWithLabel3(data, 3, True))
+
+    # 导入第四类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA'))
+    dataWithLabel4 = ConcatLabel(data, 4, True)
+    dataWithLabel4 = np.array(dataWithLabel4(data, 4, True))
+
+    # 导入第五类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA'))
+    dataWithLabel5 = ConcatLabel(data, 5, True)
+    dataWithLabel5 = np.array(dataWithLabel5(data, 5, True))
+
+    # 导入第六类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA'))
+    dataWithLabel6 = ConcatLabel(data, 6, True)
+    dataWithLabel6 = np.array(dataWithLabel6(data, 6, True))
+
+    # 导入第七类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA'))
+    dataWithLabel7 = ConcatLabel(data, 7, True)
+    dataWithLabel7 = np.array(dataWithLabel7(data, 7, True))
+
+    # 导入第八类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA'))
+    dataWithLabel8 = ConcatLabel(data, 8, True)
+    dataWithLabel8 = np.array(dataWithLabel8(data, 8, True))
+
+    # 导入第九类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA'))
+    dataWithLabel9 = ConcatLabel(data, 9, True)
+    dataWithLabel9 = np.array(dataWithLabel9(data, 9, True))
+
+    data_all = tf.concat(
+        [dataWithLabel1, dataWithLabel2, dataWithLabel3, dataWithLabel4, dataWithLabel5, dataWithLabel6, dataWithLabel7,
+         dataWithLabel8, dataWithLabel9], axis=1)
+
+    data_all = np.array(data_all)
+    print(data_all.shape)
+    data_all =tf.transpose(data,[1,2,0])
+    print(data_all.shape)
+    data_train = tf.random.shuffle(data_all)
+    print(data_train)
+    data_all = tf.transpose(data, [2, 0, 1])
+    data_new = PieceAndBag(data_train, True)
+    (train_data, train_label), (test_data, test_label) = data_new(data_train, True)
+    train_data = np.array(train_data)
+    train_label = np.array(train_label)
+    test_data = np.array(test_data)
+    test_label = np.array(test_label)
+
+    print(train_data.shape)
+    print(train_label.shape)
+    print(test_data.shape)
+    print(test_label.shape)

TensorFlow_eaxmple/datadeal_try/loadData.py

@@ -0,0 +1,155 @@
+import numpy as np
+import os
+import tensorflow as tf
+
+'''
+class DataDeal1：
+这是一个处理data的类
+使用示例：DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+作用是将后缀为.dat的文件按节点个数(head)和加载的点数n的不同解析为(3,108,dim/108)维的文件
+需要传入的参数有：
+head:节点头数   int类型
+filename:放置.dat文件的根路径   string类型
+'''
+
+
+class DataDeal1(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        result = [[[] * self.head] * k.shape[0]]
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum0 = [[] for i in range(self.head)]
+        sum1 = [[] for i in range(self.head)]
+        sum2 = [[] for i in range(self.head)]
+        '''sum0=[None]*self.head
+        sum1 = [None] * self.head
+        sum2 = [None] * self.head
+        for i in range(self.head):
+            sum0[i] = [0]* 204800
+            sum1[i] = [0]* 204800
+            sum2[i] = [0]* 204800'''
+        z = 0
+        for filename in Z:
+            # 将二进制文件读成np数组
+            each_data = np.fromfile(filename, dtype=np.float32)
+            data = np.array(each_data)
+            # 创建一个二维空数组
+            data_true = [[] for i in range(self.head)]
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            for i in range(self.head):
+                data_true[i] = data[i::self.head]
+                # 如果第一次是空数剧，则无法进行拼接
+                if z == 0:
+                    sum0[i] = data_true[i]
+                if z == 4:
+                    sum1[i] = data_true[i]
+                if z == 8:
+                    sum2[i] = data_true[i]
+            # 将二维空数组重新整合成np.array的形式
+            data_true = np.array(data_true)
+            if z % 12 == 0 or z % 12 == 1 or z % 12 == 2 or z % 12 == 3:
+                if z != 0:
+                    sum0 = np.vstack((sum0, data_true))
+            if z % 12 == 4 or z % 12 == 6 or z % 12 == 7 or z % 12 == 5:
+                if z != 4:
+                    sum1 = np.vstack((sum1, data_true))
+            if z % 12 == 9 or z % 12 == 10 or z % 12 == 11 or z % 12 == 8:
+                if z != 8:
+                    sum2 = np.vstack((sum2, data_true))
+            z = z + 1
+        sum0 = np.array(sum0)
+        sum1 = np.array(sum1)
+        sum2 = np.array(sum2)
+        print("sum0:", sum0.shape)
+        print("sum1:", sum1.shape)
+        print("sum2:", sum2.shape)
+        return sum0, sum1, sum2
+
+
+'''
+class DataDeal2：
+这是一个处理data的类
+使用示例：DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+作用是将后缀为.dat的文件按节点个数(head)解析为(3*108,dim/108)维的文件
+需要传入的参数有：
+head:节点头数   int类型
+filename:放置.dat文件的根路径   string类型
+'''
+
+
+class DataDeal2(object):
+    voiceArray = []
+
+    def __init__(self, head, filename):
+        self.head = head
+        self.name = filename
+        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
+        L = []
+        for root, dir, filename in os.walk(self.name):
+            for file in filename:
+                if os.path.splitext(file)[1] == '.dat':
+                    L.append(file)
+        self.array = L
+
+    def __call__(self, head, filename, **kwargs):
+        k = np.array(self.array)
+        Z = []
+        for file in self.array:
+            # 构建文件名
+            filename = self.name + '/' + file
+            Z.append(filename)
+        sum = [[] for i in range(k.shape[0]//self.head)]
+        z = 0
+        for filename in Z:
+            # 将二进制文件读成np数组
+            each_data = np.fromfile(filename, dtype=np.float32)
+            data = np.array(each_data)
+            # 创建一个二维空数组
+            data_true = [[] for i in range(self.head)]
+            # 数据按节点个数分成head个分别存入二维空数组的各行
+            for i in range(self.head):
+                data_true[i] = data[i::self.head]
+
+            data_true = np.array(data_true)
+            # 将二维空数组重新整合成np.array的形式
+            data_true = data_true[1]
+            data_true = np.array(data_true)
+            # 创建一个二维空数组
+            data_true_new=[[] for i in range(data_true.shape[0]//4096)]
+            for i in range(data_true.shape[0]//4096):
+                data_true_new[i] = data_true[i::data_true.shape[0]//4096]
+            # 如果第一次是空数剧，则无法进行拼接
+            if z == 0:
+                sum = data_true_new
+            data_true_new = np.array(data_true_new)
+            # print(data_true_new.shape)
+            if z != 0:
+                sum = np.vstack((sum, data_true_new))
+            z = z + 1
+
+        sum = np.array(sum)
+        print("sum:", sum.shape)
+        return sum
+
+
+if __name__ == '__main__':
+    data0 = DataDeal2(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
+    print("data:", data.shape)

TensorFlow_eaxmple/datadeal_try/test.py

@@ -0,0 +1,40 @@
+'''name='r'+''+E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA\dds测试故障库4.6#0001.dat+''
+print(name)'''
+'''创建一个获取文件夹下所有后缀名为.dat得文件'''
+import os
+import numpy as np
+
+
+def filename_all(name):
+    L = []
+    for root, dir, filename in os.walk(name):
+        for file in filename:
+            if os.path.splitext(file)[1] == '.dat':
+                L.append(file)
+    for file in L:
+        print(name + file)
+    # print(L.shape[0])
+
+
+'''filename_all('E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
+k=r'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'''''
+
+# 读取文件
+voiceArray = []
+filename = 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA\dds测试故障库4.6#0001.dat'
+each_data = np.fromfile(filename, dtype=np.float32)
+print("each_data:",each_data)
+
+# 将voiceArray转化为np的数组
+data = np.array(each_data)
+print("data",data)
+print("data_shape:",data.shape)
+# 创建一个二维空数组
+data_true = [[] for i in range(9)]
+# 数据按节点个数分成head个分别存入二维空数组的各行
+for i in range(9):
+    data_true[i] = data[i::9]
+# 将二维空数组重新整合成np.array的形式
+data_true = np.array(data_true)
+print(data_true.shape)
+print(data_true)

TensorFlow_eaxmple/datadeal_try/test_new.py

@@ -0,0 +1,83 @@
+from matplotlib import numpy
+import matplotlib.pyplot as plot
+import numpy as np
+import tensorflow as tf
+import os
+import loadData
+import labeled_and_piece
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'))
+    dataWithLabel1 = labeled_and_piece.ConcatLabel(data, 1, True)
+    dataWithLabel1 = np.array(dataWithLabel1(data, 1, True))
+
+    # 导入第二类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
+    dataWithLabel2 = labeled_and_piece.ConcatLabel(data, 2, True)
+    dataWithLabel2 = np.array(dataWithLabel2(data, 2, True))
+
+    # 导入第三类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA'))
+    dataWithLabel3 = labeled_and_piece.ConcatLabel(data, 3, True)
+    dataWithLabel3 = np.array(dataWithLabel3(data, 3, True))
+
+    # 导入第四类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA'))
+    dataWithLabel4 = labeled_and_piece.ConcatLabel(data, 4, True)
+    dataWithLabel4 = np.array(dataWithLabel4(data, 4, True))
+
+    # 导入第五类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA'))
+    dataWithLabel5 = labeled_and_piece.ConcatLabel(data, 5, True)
+    dataWithLabel5 = labeled_and_piece.np.array(dataWithLabel5(data, 5, True))
+
+    # 导入第六类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA'))
+    dataWithLabel6 = labeled_and_piece.ConcatLabel(data, 6, True)
+    dataWithLabel6 = np.array(dataWithLabel6(data, 6, True))
+
+    # 导入第七类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA'))
+    dataWithLabel7 = labeled_and_piece.ConcatLabel(data, 7, True)
+    dataWithLabel7 = np.array(dataWithLabel7(data, 7, True))
+
+    # 导入第八类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA'))
+    dataWithLabel8 = labeled_and_piece.ConcatLabel(data, 8, True)
+    dataWithLabel8 = np.array(dataWithLabel8(data, 8, True))
+
+    # 导入第九类故障并打标签
+    data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA')
+    data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA'))
+    dataWithLabel9 = labeled_and_piece.ConcatLabel(data, 9, True)
+    dataWithLabel9 = np.array(dataWithLabel9(data, 9, True))
+
+    data_all = tf.concat(
+        [dataWithLabel1, dataWithLabel2, dataWithLabel3, dataWithLabel4, dataWithLabel5, dataWithLabel6, dataWithLabel7,
+         dataWithLabel8, dataWithLabel9], axis=1)
+
+    data_all = np.array(data_all)
+    print(data_all.shape)
+    data_all =tf.transpose(data,[1,2,0])
+    print(data_all.shape)
+    data_train = tf.random.shuffle(data_all)
+    print(data_train)
+    data_all = tf.transpose(data, [2, 0, 1])
+    data_new = labeled_and_piece.PieceAndBag(data_train, True)
+    (train_data, train_label), (test_data, test_label) = data_new(data_train, True)
+    train_data = np.array(train_data)
+    train_label = np.array(train_label)
+    test_data = np.array(test_data)
+    test_label = np.array(test_label)
+
+    print(train_data.shape)
+    print(train_label.shape)
+    print(test_data.shape)
+    print(test_label.shape)
+

TensorFlow_eaxmple/dataset/cora/README

@@ -0,0 +1,29 @@
+This directory contains the a selection of the Cora dataset (www.research.whizbang.com/data).
+
+The Cora dataset consists of Machine Learning papers. These papers are classified into one of the following seven classes:
+		Case_Based
+		Genetic_Algorithms
+		Neural_Networks
+		Probabilistic_Methods
+		Reinforcement_Learning
+		Rule_Learning
+		Theory
+
+The papers were selected in a way such that in the final corpus every paper cites or is cited by atleast one other paper. There are 2708 papers in the whole corpus. 
+
+After stemming and removing stopwords we were left with a vocabulary of size 1433 unique words. All words with document frequency less than 10 were removed.
+
+
+THE DIRECTORY CONTAINS TWO FILES:
+
+The .content file contains descriptions of the papers in the following format:
+
+		<paper_id> <word_attributes>+ <class_label>
+
+The first entry in each line contains the unique string ID of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last entry in the line contains the class label of the paper.
+
+The .cites file contains the citation graph of the corpus. Each line describes a link in the following format:
+
+		<ID of cited paper> <ID of citing paper>
+
+Each line contains two paper IDs. The first entry is the ID of the paper being cited and the second ID stands for the paper which contains the citation. The direction of the link is from right to left. If a line is represented by "paper1 paper2" then the link is "paper2->paper1". 

TensorFlow_eaxmple/dataset/dataTest.csv

@@ -0,0 +1,201 @@
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+20201,5.6,6.29,6.11,2.64,0.11,4.08,2.44,7.04,5.6,5.6,5.17,0.29,3.45,5.17,2.05,3.71,5.26,3.77,4.46,5.31,7.22,2.76,0.64,0.85,5.53,8.85,9.51,2.43,3.32,8.11,7.43,0,2.7,1.52,0.58,5.46,5.71,7.37,0.25,0.51,7.18,7.9,9.2,5.75,0,1.15,5.26,0.61,1.27,0.78,1.99,4.32,6.65,3.18,9.34,0.85,1.7,6.64,3.98,0.44,0,1.33,0.68,0,7.43,22.97,4.73,10.81,0,6.51,6.69,5.28,0.11,6.36,5.28,2.3,0.43,0.14,4.02,2.11,6.26,5.76,5.6,5.87,1.27,2.71,2.76,7.22,6.79,3.18,1.55,4.87,12.39,6.19,7.52,9.96,0.22,6.08,6.08,0,1.35,4.05,0,0,2.7,6.51,5.53,0.25,7.7,3.58,1.29,8.19,6.61,3.88,7.18,0,4.21,8.31,6.32,6.09,3.71,6.09,2.76,5.52,8.07,7.43,2.55,5.1,3.18,3.18,1.91,4.03,1.11,3.76,5.53,0,1.55,2.88,0.44,0.68,2.7,8.11,0,3.38,5.71,6.51,4.08,7.04,5.6,7.18,5.75,2.3,4.02,2.11,4.21,6.26,5.87,0.78,4.32,2.76,7.22,8.07,7.43,7.22,5.1,0.64,6.51,5.6,7.7,0.25,7.18,3.88,9.2,0,5.17,5.26,5.26,8.31,6.32,3.71,3.77,0.61,5.52,3.18,1.7,1.52,0.58,5.46,6.69,5.28,5.53,6.29,7.37,0.25,6.11,3.58,0.11,5.28,5.6,5.17,0.29,0,0.43,3.71,5.76,5.6,1.27,6.09,1.27,6.09,2.76,4.46,5.31,6.79,2.55,3.18,3.18,0.85,1.91,2.64,0.11,0.51,6.36,2.44,1.29,7.9,8.19,6.61,3.45,0.14,1.15,2.05,2.71,1.99,6.65,9.34,2.76,3.18,0.85,4.03,0,0,0,0,4,0,0,0,0,0,0,0,0,0,17,2,0,0,0,0,0,0,0,0,0,0,0,20,4,0,0,0,5,-1,0,0,0,0,0,0,0,0,3,0,3,0,0,-1,13,0,0,2,0,1,1,49.77,60.17,0,2,0,-1,0,0,30,0,21.33,123,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,0.52,1,3,1,1,1,31.13958,112.2465,0,0,0,0,0,17,0,300,177,1235,614,985,3.5,5.62,7.04,77.51,0,0,9.54,30.69,479.83,29.48,45.78,0,436.13,10.06,216.85,185.9,0,293.93,0,26.36,29.31,17.96,895,382.2,29,5.09,593,840,2,7,92,105,0,1,0,0,95,5,1433,30,53,1,7,17,21,8,38,2,248.5,175,197,0,0,3,49,175,54,11,9,1,38,3,37,172,176,58,102.83,145.67,3,1000,31,14,4,3,2,2,1,1,0,0,0,2,0,1,0,8.19,48,8.49,1.35,79.91,0,18.21,18.21,232,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,0,15,10,12,33,0,3,0,0,0,0,0,0,10,4,61,110,269,362,261,572,171,123,199,0,14,631,833,0,0,0,0,11,0,0,0,5,0,0,0,0,5,0,0,0,0,0,0,11,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,7,-1,1,0,-1,24,3,76,36,29,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,7,5,0,1,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,39,-1,-1,7,-1,-1,5,2,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,42,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-1,7,0,0,0,8,1,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,1,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,-1,-1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1,0,0,1,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,3,0,0,0,1,0,0,0,0,0,0,0,0,4,0,3,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,292,239.67,477,51,336.44,0,0,0,0,0,209.14,82.87,177.9,282.95,0,1,1,1,17.78,17.79,17.79,0,1,1,1,1,-1,-1,-1,1,0,0,-1,-1,-1,0,112,68,100,52,2,5,14,4,1,7,13,6,15,0,21,2,38,5,134,61,33,17,0,21,2,1,0,6,45,5,0,3,2,2,14,5,0,0,0,104,0,2,16,8,3,9,12,22,137,0,12,0,1,2,0,-1,1,-1,-1,1,1,1,1,-1,1,1,1,1,-1,1,1,1,25,14,-1,-1,-1,0,0,2,0,2,0,2,0,0,0,0,0,0,0,0,2,0,0,0,32,0,36,0,0,0,0,0,0,0,0,5,0,0,0,32,0,0,4,0,0,0,0,0,0,0,0,0,0,0,4,1,1,1,0,22,1,23,23,18,23,23,1,1,1,23,23,7,7,7,7,2,7,7,7,8,8,7,7,3,1,7,8,1,2,7,23

TensorFlow_eaxmple/dataset/历年沿海主要港口货物吞吐量.csv

@@ -0,0 +1,20 @@
+总计,31154,37937,40604,45587,49025,48321,53220,60380,67835,74370,80166,85152,90822,92237,105162,125603,142634,166628,201126,246074,292777,342191
+按沿海主要港口分,,21.77%,7.03%,12.27%,7.54%,-1.44%,10.14%,13.45%,12.35%,9.63%,7.79%,6.22%,6.66%,1.56%,14.01%,19.44%,13.56%,16.82%,20.70%,22.35%,18.98%,16.88%
+　大连,4381,4429,4610,4853,5092,4952,5472,5909,5959,6212,6417,6427,7044,7515,8505,9084,10047,10851,12602,14516,17085,20046
+　营口,98,113,116,181,200,237,383,509,682,764,1156,1493,1605,1758,1945,2268,2520,3127,4009,5978,7537,9477
+　秦皇岛,4419,4873,5379,5812,6565,6945,7236,8121,7808,8207,8382,8312,7862,7792,8261,9743,11302,11167,12562,15037,16900,20489
+　天津,1856,1818,1725,2109,2437,2063,2378,2929,3719,4652,5787,6188,6789,6818,7298,9566,11369,12906,16182,20619,24069,25760
+　烟台,689,691,696,780,703,668,833,1114,1106,1266,1361,1430,1560,1511,1646,1774,2190,2689,2936,3431,4506,6076
+　青岛,2611,2801,3028,3109,3112,3034,3055,3125,3545,4213,5103,6003,6916,7018,7257,8636,10398,12213,14090,16265,18678,22415
+　日照,,,,,,925,1080,1202,1317,1429,1452,1575,1650,1724,2003,2674,2933,3136,4507,5108,8421,11007
+　连云港,929,949,894,1114,1126,1137,1213,1359,1417,1589,1716,1583,1652,1776,2017,2708,3058,3316,3752,4352,6016,7232
+　上海,11291,12604,12833,13320,14604,13959,14679,16297,17596,16581,16567,16402,16397,16388,18641,20440,22099,26384,31621,37896,44317,47040
+　宁波-舟山,1040,1795,1940,2002,2209,2554,3390,4367,5321,5850,6853,7639,8220,8707,9660,11547,12852,15398,18543,22586,26881,42387
+　汕头,201,231,251,286,282,279,355,460,662,708,716,832,884,904,1191,1284,1309,1380,1470,1576,1736,2015
+　广州,1772,2595,3314,4735,4704,4163,4668,5464,6552,7158,7299,7450,7518,7863,10157,11128,12823,15324,17187,21520,25036,30282
+　湛江,1231,1296,1424,1531,1610,1557,1667,1644,1953,2124,1885,1769,2050,1844,1751,2038,2205,2627,2866,3780,4647,5664
+　海口,170,174,198,241,256,288,321,413,580,699,468,537,486,470,674,808,888,1073,1329,1416,2118,2127
+　八所,388,391,405,393,410,431,350,377,384,355,275,265,336,358,380,378,342,343,425,548,486,479
+　三亚,78,64,64,59,64,37,37,39,65,79,42,31,30,33,27,48,71,49,61,58,49,50
+　其他港口,,3113,3727,5062,5651,5092,6103,7051,9169,12484,14687,17216,19823,19758,23749,31479,36228,44645,56984,71388,84295,89645
+注：从2006年起，宁波-舟山港包括原宁波港和舟山港，以往年度数据为宁波港数据。,,,,,,,,,,,,,,,,,,,,,,

TensorFlow_eaxmple/dataset/某地降雨量.csv

@@ -0,0 +1,13 @@
+,1,2,3,4,5,6,7,8,9,10,11,12
+2000,177,192,219,127,533,2445,1923,2300,1662,1190,74,19
+2001,8,86,43,70,2041,3375,1895,1482,2552,2306,357,0
+2002,295,13,60,157,2134,1712,1830,2508,1534,625,137,20
+2003,153,13,11,151,925,3292,1534,2302,1917,328,109,99
+2004,61,0,0,833,800,2436,4163,1804,3097,365,311,18
+2005,21,66,437,153,218,2199,4044,2045,2943,1376,0,100
+2006,0,168,3,134,1725,2600,1591,746,1958,1640,34,77
+2007,221,115,0,718,1557,766,3684,2381,1964,1227,278,5
+2008,221,148,268,209,1356,2187,3576,805,621,2561,219,31
+2009,97,13,127,303,978,2756,2124,2482,1462,670,10,36
+2010,0,0,3,137,651,1907,3119,1842,2074,1769,279,35
+2011,199,0,64,169,697,2713,865,1809,2125,574,104,20

TensorFlow_eaxmple/matplotlib_try/pylot.py

@@ -0,0 +1,13 @@
+import numpy as np
+import pylab
+import scipy.stats as stats
+
+data = np.mat([[1, 200, 105, 3, False], [2, 165, 80, 2, False],
+              [3, 184.5, 120, 2, False], [4, 116, 70.8, 1, False], [5, 270, 150, 4, True]])
+coll=[]
+for row in data:
+    coll.append(row[0,1])
+
+#scipy是专门的机器学习的数据处理包，probplot计算了coll数据集中数据在正态分布下的分离程度
+stats.probplot(coll,plot=pylab)
+pylab.show()

TensorFlow_eaxmple/matplotlib_try/scatter.py

@@ -0,0 +1,32 @@
+
+import matplotlib.pylab as plot
+import pandas as pd
+
+data = pd.DataFrame([[1, 200, 105, 3, False], [2, 165, 80, 2, False],
+              [3, 184.5, 120, 2, False], [4, 116, 70.8, 1, False], [5, 270, 150, 4, True]])
+print(data)
+print('----------------')
+#第一行的前三列
+dataRow1=data.iloc[1,0:3]
+#第二行的前三列
+dataRow2=data.iloc[2,0:3]
+print(dataRow1)
+print(dataRow2)
+print('----------------')
+plot.scatter(dataRow1,dataRow2)
+plot.xlabel("Attribute1")
+plot.ylabel("Attribute2")
+plot.show()
+
+dataRow3=data.iloc[3,0:3]
+plot.scatter(dataRow2,dataRow3)
+plot.xlabel("Attribute2")
+plot.ylabel("Attribute3")
+plot.show()
+
+
+array=dataFile.iloc[:,10:16].values
+boxplot(array)
+plot.xlabel("Attribute")
+plot.ylabel("score")
+show()

TensorFlow_eaxmple/numpy_try/np_mat.py

@@ -0,0 +1,14 @@
+import numpy as np
+
+data = np.mat([[1, 200, 105, 3, False], [2, 165, 80, 2, False],
+              [3, 184.5, 120, 2, False], [4, 116, 70.8, 1, False], [5, 270, 150, 4, True]])
+#第一行第四个数和第五个数
+print(data[0,3])
+print(data[0,4])
+row = 0
+print(data)
+for line in data:
+    row += 1
+#列数和size大小
+print(row)
+print(data.size)

TensorFlow_eaxmple/numpy_try/np_math.py

@@ -0,0 +1,15 @@
+import numpy as np
+
+data = np.mat([[1, 200, 105, 3, False], [2, 165, 80, 2, False],
+              [3, 184.5, 120, 2, False], [4, 116, 70.8, 1, False], [5, 270, 150, 4, True]])
+coll=[]
+for row in data:
+    print(row)
+    coll.append(row[0,1])
+
+print(coll)
+print('--------------')
+print(np.sum(coll))  #数据求和
+print(np.mean(coll)) #数据求平均
+print(np.std(coll))  #数据求标准差
+print(np.var(coll))  #数据求方差