tf2.0-keras

#读取模型
fashion_mnist = tf.keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

#获得图片大小
train_images.shape
#打印图例
import numpy as np
import matplotlib.pyplot as plt
def plotImages(images_arr):
    fig, axes = plt.subplots(1, 5, figsize=(10,10))
    axes = axes.flatten()
    for img, ax in zip( images_arr, axes):
        ax.imshow(img)
        ax.axis('off')
    plt.tight_layout()
    plt.show()
plotImages(train_images[:5])

#归一化
train_images = train_images / 255.0
test_images = test_images / 255.0

#全链接层模型
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu', bias=False, trainable=False),
    tf.keras.layers.Dense(10, activation='softmax')
])
#模型总结
model.summary()
#编译
model.compile(optimizer='adam',
      loss='sparse_categorical_crossentropy', categorical_crossentropy->[1,0,0,0,0]
      metrics=['accuracy']) 
      
#训练
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))
#模型权重
model.variables

# 保存权重
model.save_weights('./fashion_mnist/my_checkpoint')

# 恢复权重
model.load_weights('./fashion_mnist/my_checkpoint')
# model1.load_weights('./fashion_mnist/my_checkpoint') 

# 预测
loss,acc = model.evaluate(test_images,  test_labels, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

#保存整个模型
model.save('my_model.h5')
new_model = tf.keras.models.load_model('my_model.h5')
loss, acc = new_model.evaluate(test_images,  test_labels, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

# 在文件名中包含 epoch (使用 `str.format`)
checkpoint_path = "fashion_mnist_1/cp-{epoch:04d}.ckpt"

# 创建一个回调，每个epoch保存模型的权重
cp_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_path, 
    save_weights_only=True,
    period=1) #save_freq = ‘epoch'/n samples 60000 100 600

# 使用 `checkpoint_path` 格式保存权重
model.save_weights(checkpoint_path.format(epoch=0))

# 使用新的回调训练模型
model.fit(train_images, 
              train_labels,
              epochs=5, 
              callbacks=[cp_callback],
              validation_data=(test_images,test_labels))
              
#CNN模型
train_images = train_images[..., tf.newaxis]
test_images = test_images[..., tf.newaxis]

model1 = tf.keras.Sequential()
model1.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model1.add(tf.keras.layers.MaxPooling2D((2, 2)))
model1.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
model1.add(tf.keras.layers.MaxPooling2D((2, 2)))
model1.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
model1.add(tf.keras.layers.Flatten())
model1.add(tf.keras.layers.Dense(256, activation='relu'))
model1.add(tf.keras.layers.Dense(10, activation='softmax'))

model1.compile(optimizer='adam',
      loss='sparse_categorical_crossentropy',
      metrics=['accuracy'])
      
model1.fit(train_images, train_labels, 
           batchsize=64,
           epochs=10, validation_data=(test_images, test_labels))

#RNN
model2 = tf.keras.Sequential()
model2.add(tf.keras.layers.LSTM(128,input_shape=(None,28))) batchsize,28,28
# model2.add(tf.keras.layers.LSTM(128, return_sequences=True))
#(hidden size * (hidden size + input_dim ) + hidden size) *4
model2.add(tf.keras.layers.Dense(10, activation='softmax'))

model2.compile(optimizer='adam',
      loss='sparse_categorical_crossentropy',
      metrics=['accuracy'])

model2.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))