# 模型构造inputs = keras.Input(shape=(784,), name='mnist_input')h1 = layers.Dense(64, activation='relu')(inputs)h1 = layers.Dense(64, activation='relu')(h1)outputs = layers.Dense(10, activation='softmax')(h1)model = keras.Model(inputs, outputs)# keras.utils.plot_model(model, 'net001.png', show_shapes=True)model.compile(optimizer=keras.optimizers.RMSprop(),    loss=keras.losses.SparseCategoricalCrossentropy(),    metrics=[keras.metrics.SparseCategoricalAccuracy()])# 载入数据(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()x_train = x_train.reshape(60000, 784).astype('float32') /255x_test = x_test.reshape(10000, 784).astype('float32') /255x_val = x_train[-10000:]y_val = y_train[-10000:]x_train = x_train[:-10000]y_train = y_train[:-10000]# 训练模型history = model.fit(x_train, y_train, batch_size=64, epochs=3,   validation_data=(x_val, y_val))print('history:')print(history.history)result = model.evaluate(x_test, y_test, batch_size=128)print('evaluate:')print(result)pred = model.predict(x_test[:2])print('predict:')print(pred)

# 这是一个简单的示例，显示如何实现CatgoricalTruePositives指标，该指标计算正确分类为属于给定类的样本数量class CatgoricalTruePostives(keras.metrics.Metric): def __init__(self, name='binary_true_postives', **kwargs):  super(CatgoricalTruePostives, self).__init__(name=name, **kwargs)  self.true_postives = self.add_weight(name='tp', initializer='zeros')   def update_state(self, y_true, y_pred, sample_weight=None):  y_pred = tf.argmax(y_pred)  y_true = tf.equal(tf.cast(y_pred, tf.int32), tf.cast(y_true, tf.int32))    y_true = tf.cast(y_true, tf.float32)    if sample_weight is not None:   sample_weight = tf.cast(sample_weight, tf.float32)   y_true = tf.multiply(sample_weight, y_true)     return self.true_postives.assign_add(tf.reduce_sum(y_true))  def result(self):  return tf.identity(self.true_postives)  def reset_states(self):  self.true_postives.assign(0.)  model.compile(optimizer=keras.optimizers.RMSprop(1e-3),    loss=keras.losses.SparseCategoricalCrossentropy(),    metrics=[CatgoricalTruePostives()])model.fit(x_train, y_train,   batch_size=64, epochs=3)

# 以定义网络层的方式添加网络lossclass ActivityRegularizationLayer(layers.Layer): def call(self, inputs):  self.add_loss(tf.reduce_sum(inputs) * 0.1)  return inputsinputs = keras.Input(shape=(784,), name='mnist_input')h1 = layers.Dense(64, activation='relu')(inputs)h1 = ActivityRegularizationLayer()(h1)h1 = layers.Dense(64, activation='relu')(h1)outputs = layers.Dense(10, activation='softmax')(h1)model = keras.Model(inputs, outputs)# keras.utils.plot_model(model, 'net001.png', show_shapes=True)model.compile(optimizer=keras.optimizers.RMSprop(),    loss=keras.losses.SparseCategoricalCrossentropy(),    metrics=[keras.metrics.SparseCategoricalAccuracy()])model.fit(x_train, y_train, batch_size=32, epochs=1)

# 也可以以定义网络层的方式添加要统计的metricclass MetricLoggingLayer(layers.Layer): def call(self, inputs):  self.add_metric(keras.backend.std(inputs),      name='std_of_activation',      aggregation='mean')    return inputsinputs = keras.Input(shape=(784,), name='mnist_input')h1 = layers.Dense(64, activation='relu')(inputs)h1 = MetricLoggingLayer()(h1)h1 = layers.Dense(64, activation='relu')(h1)outputs = layers.Dense(10, activation='softmax')(h1)model = keras.Model(inputs, outputs)# keras.utils.plot_model(model, 'net001.png', show_shapes=True)model.compile(optimizer=keras.optimizers.RMSprop(),    loss=keras.losses.SparseCategoricalCrossentropy(),    metrics=[keras.metrics.SparseCategoricalAccuracy()])model.fit(x_train, y_train, batch_size=32, epochs=1)

# 也可以直接在model上面加# 也可以以定义网络层的方式添加要统计的metricclass MetricLoggingLayer(layers.Layer): def call(self, inputs):  self.add_metric(keras.backend.std(inputs),      name='std_of_activation',      aggregation='mean')    return inputsinputs = keras.Input(shape=(784,), name='mnist_input')h1 = layers.Dense(64, activation='relu')(inputs)h2 = layers.Dense(64, activation='relu')(h1)outputs = layers.Dense(10, activation='softmax')(h2)model = keras.Model(inputs, outputs)model.add_metric(keras.backend.std(inputs),      name='std_of_activation',      aggregation='mean')model.add_loss(tf.reduce_sum(h1)*0.1)# keras.utils.plot_model(model, 'net001.png', show_shapes=True)model.compile(optimizer=keras.optimizers.RMSprop(),    loss=keras.losses.SparseCategoricalCrossentropy(),    metrics=[keras.metrics.SparseCategoricalAccuracy()])model.fit(x_train, y_train, batch_size=32, epochs=1)

model.fit(x_train, y_train, batch_size=32, epochs=1, validation_split=0.2)

def get_compiled_model(): inputs = keras.Input(shape=(784,), name='mnist_input') h1 = layers.Dense(64, activation='relu')(inputs) h2 = layers.Dense(64, activation='relu')(h1) outputs = layers.Dense(10, activation='softmax')(h2) model = keras.Model(inputs, outputs) model.compile(optimizer=keras.optimizers.RMSprop(),     loss=keras.losses.SparseCategoricalCrossentropy(),     metrics=[keras.metrics.SparseCategoricalAccuracy()]) return modelmodel = get_compiled_model()train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))val_dataset = val_dataset.batch(64)# model.fit(train_dataset, epochs=3)# steps_per_epoch 每个epoch只训练几步# validation_steps 每次验证，验证几步model.fit(train_dataset, epochs=3, steps_per_epoch=100,   validation_data=val_dataset, validation_steps=3)

# 增加第5类的权重import numpy as np# 样本权重model = get_compiled_model()class_weight = {i:1.0 for i in range(10)}class_weight[5] = 2.0print(class_weight)model.fit(x_train, y_train,   class_weight=class_weight,   batch_size=64,   epochs=4)# 类权重model = get_compiled_model()sample_weight = np.ones(shape=(len(y_train),))sample_weight[y_train == 5] = 2.0model.fit(x_train, y_train,   sample_weight=sample_weight,   batch_size=64,   epochs=4)

# tf.data数据model = get_compiled_model()sample_weight = np.ones(shape=(len(y_train),))sample_weight[y_train == 5] = 2.0train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train,             sample_weight))train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))val_dataset = val_dataset.batch(64)model.fit(train_dataset, epochs=3, )

image_input = keras.Input(shape=(32, 32, 3), name='img_input')timeseries_input = keras.Input(shape=(None, 10), name='ts_input')x1 = layers.Conv2D(3, 3)(image_input)x1 = layers.GlobalMaxPooling2D()(x1)x2 = layers.Conv1D(3, 3)(timeseries_input)x2 = layers.GlobalMaxPooling1D()(x2)x = layers.concatenate([x1, x2])score_output = layers.Dense(1, name='score_output')(x)class_output = layers.Dense(5, activation='softmax', name='class_output')(x)model = keras.Model(inputs=[image_input, timeseries_input],     outputs=[score_output, class_output])keras.utils.plot_model(model, 'multi_input_output_model.png'      , show_shapes=True)

# 可以为模型指定不同的loss和metricsmodel.compile( optimizer=keras.optimizers.RMSprop(1e-3), loss=[keras.losses.MeanSquaredError(),   keras.losses.CategoricalCrossentropy()])# 还可以指定loss的权重model.compile( optimizer=keras.optimizers.RMSprop(1e-3), loss={'score_output': keras.losses.MeanSquaredError(),   'class_output': keras.losses.CategoricalCrossentropy()}, metrics={'score_output': [keras.metrics.MeanAbsolutePercentageError(),        keras.metrics.MeanAbsoluteError()],    'class_output': [keras.metrics.CategoricalAccuracy()]}, loss_weight={'score_output': 2., 'class_output': 1.})# 可以把不需要传播的loss置0model.compile( optimizer=keras.optimizers.RMSprop(1e-3), loss=[None, keras.losses.CategoricalCrossentropy()])# Or dict loss versionmodel.compile( optimizer=keras.optimizers.RMSprop(1e-3), loss={'class_output': keras.losses.CategoricalCrossentropy()})

model = get_compiled_model()callbacks = [ keras.callbacks.EarlyStopping(  # Stop training when `val_loss` is no longer improving  monitor='val_loss',  # "no longer improving" being defined as "no better than 1e-2 less"  min_delta=1e-2,  # "no longer improving" being further defined as "for at least 2 epochs"  patience=2,  verbose=1)]model.fit(x_train, y_train,   epochs=20,   batch_size=64,   callbacks=callbacks,   validation_split=0.2)

# checkpoint模型回调model = get_compiled_model()check_callback = keras.callbacks.ModelCheckpoint( filepath='mymodel_{epoch}.h5', save_best_only=True, monitor='val_loss', verbose=1)model.fit(x_train, y_train,   epochs=3,   batch_size=64,   callbacks=[check_callback],   validation_split=0.2)

# 动态调整学习率initial_learning_rate = 0.1lr_schedule = keras.optimizers.schedules.ExponentialDecay( initial_learning_rate, decay_steps=10000, decay_rate=0.96, staircase=True)optimizer = keras.optimizers.RMSprop(learning_rate=lr_schedule)

# 使用tensorboardtensorboard_cbk = keras.callbacks.TensorBoard(log_dir='./full_path_to_your_logs')model.fit(x_train, y_train,   epochs=5,   batch_size=64,   callbacks=[tensorboard_cbk],   validation_split=0.2)

class LossHistory(keras.callbacks.Callback): def on_train_begin(self, logs):  self.losses = [] def on_epoch_end(self, batch, logs):  self.losses.append(logs.get('loss'))  print('/nloss:',self.losses[-1])  model = get_compiled_model()callbacks = [ LossHistory()]model.fit(x_train, y_train,   epochs=3,   batch_size=64,   callbacks=callbacks,   validation_split=0.2)

# Get the model.inputs = keras.Input(shape=(784,), name='digits')x = layers.Dense(64, activation='relu', name='dense_1')(inputs)x = layers.Dense(64, activation='relu', name='dense_2')(x)outputs = layers.Dense(10, activation='softmax', name='predictions')(x)model = keras.Model(inputs=inputs, outputs=outputs)# Instantiate an optimizer.optimizer = keras.optimizers.SGD(learning_rate=1e-3)# Instantiate a loss function.loss_fn = keras.losses.SparseCategoricalCrossentropy()# Prepare the training dataset.batch_size = 64train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)# 自己构造循环for epoch in range(3): print('epoch: ', epoch) for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):  # 开一个gradient tape, 计算梯度  with tf.GradientTape() as tape:   logits = model(x_batch_train)      loss_value = loss_fn(y_batch_train, logits)   grads = tape.gradient(loss_value, model.trainable_variables)   optimizer.apply_gradients(zip(grads, model.trainable_variables))     if step % 200 == 0:   print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))   print('Seen so far: %s samples' % ((step + 1) * 64))

# 训练并验证# Get modelinputs = keras.Input(shape=(784,), name='digits')x = layers.Dense(64, activation='relu', name='dense_1')(inputs)x = layers.Dense(64, activation='relu', name='dense_2')(x)outputs = layers.Dense(10, activation='softmax', name='predictions')(x)model = keras.Model(inputs=inputs, outputs=outputs)# Instantiate an optimizer to train the model.optimizer = keras.optimizers.SGD(learning_rate=1e-3)# Instantiate a loss function.loss_fn = keras.losses.SparseCategoricalCrossentropy()# Prepare the metrics.train_acc_metric = keras.metrics.SparseCategoricalAccuracy() val_acc_metric = keras.metrics.SparseCategoricalAccuracy()# Prepare the training dataset.batch_size = 64train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)# Prepare the validation dataset.val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))val_dataset = val_dataset.batch(64)# Iterate over epochs.for epoch in range(3): print('Start of epoch %d' % (epoch,))  # Iterate over the batches of the dataset. for step, (x_batch_train, y_batch_train) in enumerate(train_dataset): with tf.GradientTape() as tape:  logits = model(x_batch_train)  loss_value = loss_fn(y_batch_train, logits) grads = tape.gradient(loss_value, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables))   # Update training metric. train_acc_metric(y_batch_train, logits) # Log every 200 batches. if step % 200 == 0:  print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))  print('Seen so far: %s samples' % ((step + 1) * 64)) # Display metrics at the end of each epoch. train_acc = train_acc_metric.result() print('Training acc over epoch: %s' % (float(train_acc),)) # Reset training metrics at the end of each epoch train_acc_metric.reset_states() # Run a validation loop at the end of each epoch. for x_batch_val, y_batch_val in val_dataset: val_logits = model(x_batch_val) # Update val metrics val_acc_metric(y_batch_val, val_logits) val_acc = val_acc_metric.result() val_acc_metric.reset_states() print('Validation acc: %s' % (float(val_acc),))

##　添加自己构造的loss, 每次只能看到最新一次训练增加的lossclass ActivityRegularizationLayer(layers.Layer):  def call(self, inputs): self.add_loss(1e-2 * tf.reduce_sum(inputs)) return inputs inputs = keras.Input(shape=(784,), name='digits')x = layers.Dense(64, activation='relu', name='dense_1')(inputs)# Insert activity regularization as a layerx = ActivityRegularizationLayer()(x)x = layers.Dense(64, activation='relu', name='dense_2')(x)outputs = layers.Dense(10, activation='softmax', name='predictions')(x)model = keras.Model(inputs=inputs, outputs=outputs)logits = model(x_train[:64])print(model.losses)logits = model(x_train[:64])logits = model(x_train[64: 128])logits = model(x_train[128: 192])print(model.losses)

# 将loss添加进求导中optimizer = keras.optimizers.SGD(learning_rate=1e-3)for epoch in range(3): print('Start of epoch %d' % (epoch,)) for step, (x_batch_train, y_batch_train) in enumerate(train_dataset): with tf.GradientTape() as tape:  logits = model(x_batch_train)  loss_value = loss_fn(y_batch_train, logits)  # Add extra losses created during this forward pass:  loss_value += sum(model.losses)   grads = tape.gradient(loss_value, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) # Log every 200 batches. if step % 200 == 0:  print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))  print('Seen so far: %s samples' % ((step + 1) * 64))