TensorFlow2.0使用keras训练模型的实现

# 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') /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]

# 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)

# 也可以以定义网络层的方式添加要统计的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 = Metri<i style="color:transparent">本文来源gaodai$ma#com搞$代*码6网</i>cLoggingLayer()(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)