TensorFlow2.0使用keras訓(xùn)練模型的實(shí)現(xiàn)

更新時(shí)間：2021年02月20日 09:38:27 作者：Doit_

這篇文章主要介紹了TensorFlow2.0使用keras訓(xùn)練模型的實(shí)現(xiàn)，文中通過示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧

1.一般的模型構(gòu)造、訓(xùn)練、測試流程

# 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(xùn)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)

2.自定義損失和指標(biāo)

自定義指標(biāo)只需繼承Metric類，并重寫一下函數(shù)

_init_(self)，初始化。

update_state(self，y_true，y_pred，sample_weight = None)，它使用目標(biāo)y_true和模型預(yù)測y_pred來更新狀態(tài)變量。

result(self)，它使用狀態(tài)變量來計(jì)算最終結(jié)果。

reset_states(self)，重新初始化度量的狀態(tài)。

# 這是一個(gè)簡單的示例，顯示如何實(shí)現(xiàn)CatgoricalTruePositives指標(biāo)，該指標(biāo)計(jì)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(jì)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(jì)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)

處理使用validation_data傳入測試數(shù)據(jù)，還可以使用validation_split劃分驗(yàn)證數(shù)據(jù)

ps:validation_split只能在用numpy數(shù)據(jù)訓(xùn)練的情況下使用

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

3.使用tf.data構(gòu)造數(shù)據(jù)

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(gè)epoch只訓(xùn)練幾步
# validation_steps 每次驗(yàn)證，驗(yàn)證幾步
model.fit(train_dataset, epochs=3, steps_per_epoch=100,
   validation_data=val_dataset, validation_steps=3)

4.樣本權(quán)重和類權(quán)重

“樣本權(quán)重”數(shù)組是一個(gè)數(shù)字?jǐn)?shù)組，用于指定批處理中每個(gè)樣本在計(jì)算總損失時(shí)應(yīng)具有多少權(quán)重。它通常用于不平衡的分類問題（這個(gè)想法是為了給予很少見的類更多的權(quán)重）。當(dāng)使用的權(quán)重是1和0時(shí)，該數(shù)組可以用作損失函數(shù)的掩碼（完全丟棄某些樣本對(duì)總損失的貢獻(xiàn)）。

“類權(quán)重”dict是同一概念的更具體的實(shí)例：它將類索引映射到應(yīng)該用于屬于該類的樣本的樣本權(quán)重。例如，如果類“0”比數(shù)據(jù)中的類“1”少兩倍，則可以使用class_weight = {0：1.，1：0.5}。

# 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, )

5.多輸入多輸出模型

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()})

6.使用回調(diào)

Keras中的回調(diào)是在訓(xùn)練期間（在epoch開始時(shí)，batch結(jié)束時(shí)，epoch結(jié)束時(shí)等）在不同點(diǎn)調(diào)用的對(duì)象，可用于實(shí)現(xiàn)以下行為：

在培訓(xùn)期間的不同時(shí)間點(diǎn)進(jìn)行驗(yàn)證（超出內(nèi)置的每個(gè)時(shí)期驗(yàn)證）
定期檢查模型或超過某個(gè)精度閾值
在訓(xùn)練似乎平穩(wěn)時(shí)改變模型的學(xué)習(xí)率
在訓(xùn)練似乎平穩(wěn)時(shí)對(duì)頂層進(jìn)行微調(diào)
在培訓(xùn)結(jié)束或超出某個(gè)性能閾值時(shí)發(fā)送電子郵件或即時(shí)消息通知等等。

可使用的內(nèi)置回調(diào)有

ModelCheckpoint：定期保存模型。
EarlyStopping：當(dāng)訓(xùn)練不再改進(jìn)驗(yàn)證指標(biāo)時(shí)停止培訓(xùn)。
TensorBoard：定期編寫可在TensorBoard中顯示的模型日志（更多細(xì)節(jié)見“可視化”）。
CSVLogger：將丟失和指標(biāo)數(shù)據(jù)流式傳輸?shù)紺SV文件。
等等

6.1回調(diào)使用

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(dòng)態(tài)調(diào)整學(xué)習(xí)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)

6.2創(chuàng)建自己的回調(diào)方法

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)

7.自己構(gòu)造訓(xùn)練和驗(yàn)證循環(huán)

# 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(gè)gradient tape, 計(jì)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(xùn)練并驗(yàn)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(xùn)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(jìn)求導(dǎo)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))

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