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update tf2.0
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‎17tensorflow/tf2/__init__.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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‎17tensorflow/tf2/base_demo.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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def test_grad():
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import tensorflow as tf
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x = tf.Variable(initial_value=4.)
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with tf.GradientTape() as tape: # 在 tf.GradientTape() 的上下文内，所有计算步骤都会被记录以用于求导
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y = tf.square(x)
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y_grad = tape.gradient(y, x) # 计算y关于x的导数
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print([y, y_grad]) # 2*x = 2*4 = 8
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# y = 16, y_grad = 8
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def test_linear():
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import tensorflow as tf
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X = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
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y = tf.constant([[10.0], [20.0]])
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class Linear(tf.keras.Model):
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def __init__(self):
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super().__init__()
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self.dense = tf.keras.layers.Dense(
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units=1,
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activation=None,
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kernel_initializer=tf.zeros_initializer(),
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bias_initializer=tf.zeros_initializer()
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)
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def call(self, input):
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output = self.dense(input)
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return output
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# 以下代码结构与前节类似
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model = Linear()
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optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
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for i in range(100):
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with tf.GradientTape() as tape:
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y_pred = model(X) # 调用模型 y_pred = model(X) 而不是显式写出 y_pred = a * X + b
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loss = tf.reduce_mean(tf.square(y_pred - y))
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grads = tape.gradient(loss, model.variables) # 使用 model.variables 这一属性直接获得模型中的所有变量
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optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))
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print(model.variables)
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if __name__ == '__main__':
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test_grad()
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test_linear()

‎17tensorflow/tf2/classifier_demo.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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import matplotlib.pyplot as plt
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import tensorflow.keras as keras
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(train_data, train_label), (test_data, test_label) = keras.datasets.fashion_mnist.load_data()
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print(train_data.shape)
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print(train_label.shape)
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print("#" * 42)
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model = keras.Sequential()
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model.add(keras.layers.Flatten(input_shape=(28, 28)))
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model.add(keras.layers.Dense(128, activation='relu'))
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model.add(keras.layers.Dense(128, activation='relu'))
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model.add(keras.layers.Dense(128, activation='relu'))
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model.add(keras.layers.Dense(10, activation='softmax'))
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model.summary()
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['acc'],
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)
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his = model.fit(train_data, train_label, epochs=20, validation_data=(test_data, test_label))
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print(his)
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pred_y = model.evaluate(test_data, test_label)
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plt.plot(his.epoch, his.history.get('loss'), label='loss')
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plt.plot(his.epoch, his.history.get('val_loss'), label='val_loss')
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plt.legend()
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plt.savefig('a1.png')
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plt.close()
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plt.plot(his.epoch, his.history.get('acc'), label='acc')
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plt.plot(his.epoch, his.history.get('val_acc'), label='val_acc')
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plt.legend()
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plt.savefig('a2.png')
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plt.close()

‎17tensorflow/tf2/classifier_demo2.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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import matplotlib.pyplot as plt
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import tensorflow.keras as keras
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(train_data, train_label), (test_data, test_label) = keras.datasets.fashion_mnist.load_data()
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print(train_data.shape)
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print(train_label.shape)
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print("#" * 42)
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model = keras.Sequential()
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model.add(keras.layers.Flatten(input_shape=(28, 28)))
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model.add(keras.layers.Dense(128, activation='relu'))
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# model.add(keras.layers.Dropout(0.5))
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model.add(keras.layers.Dense(128, activation='relu'))
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# model.add(keras.layers.Dropout(0.5))
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model.add(keras.layers.Dense(128, activation='relu'))
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model.add(keras.layers.Dropout(0.5))
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model.add(keras.layers.Dense(10, activation='softmax'))
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model.summary()
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['acc'],
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)
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his = model.fit(train_data, train_label, epochs=20, validation_data=(test_data, test_label))
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print(his.history.keys())
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plt.plot(his.epoch, his.history.get('loss'), label='loss')
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plt.plot(his.epoch, his.history.get('val_loss'), label='val_loss')
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plt.legend()
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plt.savefig('b5.png')
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plt.close()
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plt.plot(his.epoch, his.history.get('acc'), label='acc')
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plt.plot(his.epoch, his.history.get('val_acc'), label='val_acc')
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plt.legend()
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plt.savefig('b6.png')
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plt.close()

‎17tensorflow/tf2/classifier_demo3.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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import matplotlib.pyplot as plt
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import tensorflow.keras as keras
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(train_data, train_label), (test_data, test_label) = keras.datasets.fashion_mnist.load_data()
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print(train_data.shape)
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print(train_label.shape)
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model = keras.Sequential()
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model.add(keras.layers.Flatten(input_shape=(28, 28)))
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model.add(keras.layers.Dense(128, activation='relu'))
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model.add(keras.layers.Dense(10, activation='softmax'))
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model.summary()
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['acc'],
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)
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his = model.fit(train_data, train_label, epochs=20, validation_data=(test_data, test_label))
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print(his.history.keys())
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plt.plot(his.epoch, his.history.get('loss'), label='loss')
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plt.plot(his.epoch, his.history.get('val_loss'), label='val_loss')
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plt.legend()
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plt.savefig('c5.png')
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plt.close()
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plt.plot(his.epoch, his.history.get('acc'), label='acc')
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plt.plot(his.epoch, his.history.get('val_acc'), label='val_acc')
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plt.legend()
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plt.savefig('c6.png')
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plt.close()

‎18tensorlayer/data/mnist/t10k-images-idx3-ubyte.gz‎

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‎18tensorlayer/data/mnist/t10k-labels-idx1-ubyte.gz‎

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‎18tensorlayer/data/mnist/train-images-idx3-ubyte.gz‎

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‎18tensorlayer/data/mnist/train-labels-idx1-ubyte.gz‎

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‎18tensorlayer/tutorial_mnist.py‎

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