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