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