TensorFlow2.0使用keras訓練模型的實現(xiàn)

# 模型構(gòu)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()])

# 載入數(shù)據(jù)
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784).astype('float32') /255
x_test = x_test.reshape(10000, 784).astype('float32') /255

x_val = x_train[-10000:]
y_val = y_train[-10000:]

x_train = x_train[:-10000]
y_train = y_train[:-10000]

# 訓練模型
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)

# 這是一個簡單的示例，顯示如何實現(xiàn)CatgoricalTruePositives指標，該指標計算正確分類為屬于給定類的樣本數(shù)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)

# 以定義網(wǎng)絡(luò)層的方式添加網(wǎng)絡(luò)loss
class ActivityRegularizationLayer(layers.Layer):
 def call(self, inputs):
  self.add_loss(tf.reduce_sum(inputs) * 0.1)
  return inputs

inputs = 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)

# 也可以以定義網(wǎng)絡(luò)層的方式添加要統(tǒng)計的metric
class MetricLoggingLayer(layers.Layer):
 def call(self, inputs):
  self.add_metric(keras.backend.std(inputs),
      name='std_of_activation',
      aggregation='mean')
  
  return inputs

inputs = 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
# 也可以以定義網(wǎng)絡(luò)層的方式添加要統(tǒng)計的metric
class MetricLoggingLayer(layers.Layer):
 def call(self, inputs):
  self.add_metric(keras.backend.std(inputs),
      name='std_of_activation',
      aggregation='mean')
  
  return inputs

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.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 model
model = 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 每個epoch只訓練幾步
# validation_steps 每次驗證，驗證幾步
model.fit(train_dataset, epochs=3, steps_per_epoch=100,
   validation_data=val_dataset, validation_steps=3)

# 增加第5類的權(quán)重
import numpy as np
# 樣本權(quán)重
model = get_compiled_model()
class_weight = {i:1.0 for i in range(10)}
class_weight[5] = 2.0
print(class_weight)
model.fit(x_train, y_train,
   class_weight=class_weight,
   batch_size=64,
   epochs=4)
# 類權(quán)重
model = get_compiled_model()
sample_weight = np.ones(shape=(len(y_train),))
sample_weight[y_train == 5] = 2.0
model.fit(x_train, y_train,
   sample_weight=sample_weight,
   batch_size=64,
   epochs=4)

# tf.data數(shù)據(jù)
model = get_compiled_model()

sample_weight = np.ones(shape=(len(y_train),))
sample_weight[y_train == 5] = 2.0

train_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)

# 可以為模型指定不同的loss和metrics
model.compile(
 optimizer=keras.optimizers.RMSprop(1e-3),
 loss=[keras.losses.MeanSquaredError(),
   keras.losses.CategoricalCrossentropy()])

# 還可以指定loss的權(quán)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.})

# 可以把不需要傳播的loss置0
model.compile(
 optimizer=keras.optimizers.RMSprop(1e-3),
 loss=[None, keras.losses.CategoricalCrossentropy()])

# Or dict loss version
model.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模型回調(diào)
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)

# 動態(tài)調(diào)整學習率
initial_learning_rate = 0.1
lr_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tensorboard
tensorboard_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 = 64
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)

# 自己構(gòu)造循環(huán)
for epoch in range(3):
 print('epoch: ', epoch)
 for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
  # 開一個gradient tape, 計算梯度
  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))

# 訓練并驗證
# Get 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 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 = 64
train_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),))

##　添加自己構(gòu)造的loss, 每次只能看到最新一次訓練增加的loss
class 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 layer
x = 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)

# 將loss添加進求導中
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))