在keras中，可以通过组合层来构建模型。模型是由层构成的图。最常见的模型类型是层的堆叠：tf.keras.Sequential.
model = tf.keras.Sequential() # Adds a densely-connected layer with 64 units to the model: model.add(layers.Dense(64, activation='relu')) # Add another: model.add(layers.Dense(64, activation='relu')) # Add a softmax layer with 10 output units: model.add(layers.Dense(10, activation='softmax'))
tf.keras.layers的参数，activation:激活函数，由内置函数的名称指定，或指定为可用的调用对象。kernel_initializer和bias_initializer：层权重的初始化方案。名称或可调用对象。kernel_regularizer和bias_regularizer:层权重的正则化方案。
# Create a sigmoid layer: layers.Dense(64, activation='sigmoid') # Or: layers.Dense(64, activation=tf.sigmoid) # A linear layer with L1 regularization of factor 0.01 applied to the kernel matrix: layers.Dense(64, kernel_regularizer=tf.keras.regularizers.l1(0.01)) # A linear layer with L2 regularization of factor 0.01 applied to the bias vector: layers.Dense(64, bias_regularizer=tf.keras.regularizers.l2(0.01)) # A linear layer with a kernel initialized to a random orthogonal matrix: layers.Dense(64, kernel_initializer='orthogonal') # A linear layer with a bias vector initialized to 2.0s: layers.Dense(64, bias_initializer=tf.keras.initializers.constant(2.0))
训练和评估
设置训练流程
构建好模型后，通过调用compile方法配置该模型的学习流程：
model = tf.keras.Sequential([ # Adds a densely-connected layer with 64 units to the model: layers.Dense(64, activation='relu'), # Add another: layers.Dense(64, activation='relu'), # Add a softmax layer with 10 output units: layers.Dense(10, activation='softmax')]) model.compile(optimizer=tf.train.AdamOptimizer(0.001), loss='categorical_crossentropy', metrics=['accuracy'])
tf.keras.Model.compile采用三个重要参数：
- optimizer:从tf.train模块向其传递优化器实例，例如tf.train.AdamOptimizer,tf.train.RMSPropOptimizer或tf.train.GradientDescentOptimizer。
- loss:损失函数。常见选择包括均方误差（mse）、categorical_crossentropy和binary_crossentropy.
- metrics:评估指标
对于小型数据集，可以使用numpy数据训练。使用fit方法使模型与训练数据拟合。tf.keras.Model.fit采用三个重要参数：
- epochs：以周期为单位进行训练。
- batch_size：此整数制定每个批次的大小。
- validation_data:验证集，监控该模型在验证数据上的达到的效果。
import numpy as np data = np.random.random((1000, 32)) labels = np.random.random((1000, 10)) val_data = np.random.random((100, 32)) val_labels = np.random.random((100, 10)) model.fit(data, labels, epochs=10, batch_size=32, validation_data=(val_data, val_labels)) Train on 1000 samples, validate on 100 samples Epoch 1/10 1000/1000 [==============================] - 0s 124us/step - loss: 11.5267 - categorical_accuracy: 0.1070 - val_loss: 11.0015 - val_categorical_accuracy: 0.0500 Epoch 2/10 1000/1000 [==============================] - 0s 72us/step - loss: 11.5243 - categorical_accuracy: 0.0840 - val_loss: 10.9809 - val_categorical_accuracy: 0.1200 Epoch 3/10 1000/1000 [==============================] - 0s 73us/step - loss: 11.5213 - categorical_accuracy: 0.1000 - val_loss: 10.9945 - val_categorical_accuracy: 0.0800 Epoch 4/10 1000/1000 [==============================] - 0s 73us/step - loss: 11.5213 - categorical_accuracy: 0.1080 - val_loss: 10.9967 - val_categorical_accuracy: 0.0700 Epoch 5/10 1000/1000 [==============================] - 0s 73us/step - loss: 11.5181 - categorical_accuracy: 0.1150 - val_loss: 11.0184 - val_categorical_accuracy: 0.0500 Epoch 6/10 1000/1000 [==============================] - 0s 72us/step - loss: 11.5177 - categorical_accuracy: 0.1150 - val_loss: 10.9892 - val_categorical_accuracy: 0.0200 Epoch 7/10 1000/1000 [==============================] - 0s 72us/step - loss: 11.5130 - categorical_accuracy: 0.1320 - val_loss: 11.0038 - val_categorical_accuracy: 0.0500 Epoch 8/10 1000/1000 [==============================] - 0s 74us/step - loss: 11.5123 - categorical_accuracy: 0.1130 - val_loss: 11.0065 - val_categorical_accuracy: 0.0100 Epoch 9/10 1000/1000 [==============================] - 0s 72us/step - loss: 11.5076 - categorical_accuracy: 0.1150 - val_loss: 11.0062 - val_categorical_accuracy: 0.0800 Epoch 10/10 1000/1000 [==============================] - 0s 67us/step - loss: 11.5035 - categorical_accuracy: 0.1390 - val_loss: 11.0241 - val_categorical_accuracy: 0.1100
使用Datasets可扩展为大型数据集或多设备训练。将tf.data.Dataset实力传递到fit方法。
tf.keras.Model.evaluate和tf.keras.Model.predict方法可以使用Numpy和tf.data.Dataset评估和预测。
tf.keras.Sequential模型是层的简单堆叠，无法表示任意模型。使用keras函数式API可以构建复杂的模型。
inputs = tf.keras.Input(shape=(32,)) # Returns a placeholder tensor # A layer instance is callable on a tensor, and returns a tensor. x = layers.Dense(64, activation='relu')(inputs) x = layers.Dense(64, activation='relu')(x) predictions = layers.Dense(10, activation='softmax')(x) #给定输入和输出的情况下实例化模型。 model = tf.keras.Model(inputs=inputs, outputs=predictions) # The compile step specifies the training configuration. model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), loss='categorical_crossentropy', metrics=['accuracy']) # Trains for 5 epochs model.fit(data, labels, batch_size=32, epochs=5)
模型子类化
在__init__方法中创建层并将他们设置为类实例的属性。在__call__方法中定义前向传播。
class MyModel(tf.keras.Model): def __init__(self, num_classes=10): super(MyModel, self).__init__(name='my_model') self.num_classes = num_classes # Define your layers here. self.dense_1 = layers.Dense(32, activation='relu') self.dense_2 = layers.Dense(num_classes, activation='sigmoid') def call(self, inputs): # Define your forward pass here, # using layers you previously defined (in `__init__`). x = self.dense_1(inputs) return self.dense_2(x) def compute_output_shape(self, input_shape): # You need to override this function if you want to use the subclassed model # as part of a functional-style model. # Otherwise, this method is optional. shape = tf.TensorShape(input_shape).as_list() shape[-1] = self.num_classes return tf.TensorShape(shape) model = MyModel(num_classes=10) # The compile step specifies the training configuration. model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), loss='categorical_crossentropy', metrics=['accuracy']) # Trains for 5 epochs. model.fit(data, labels, batch_size=32, epochs=5)
通过对tf.keras.layers.Layer进行子类化并实现以下方法来创建自定义层：
- build：创建层的权重。使用add_weight方法添加权重。
- call：定义前向传播
- compute_output_shape:指定在给定输入形状的情况下如何计算输出形状。
- 或者，可以通过get_config方法和from_config类方法序列化层。
class MyLayer(layers.Layer): def __init__(self, output_dim, **kwargs): self.output_dim = output_dim super(MyLayer, self).__init__(**kwargs) def build(self, input_shape): shape = tf.TensorShape((input_shape[1], self.output_dim)) # Create a trainable weight variable for this layer. self.kernel = self.add_weight(name='kernel', shape=shape, initializer='uniform', trainable=True) # Be sure to call this at the end super(MyLayer, self).build(input_shape) def call(self, inputs): return tf.matmul(inputs, self.kernel) def compute_output_shape(self, input_shape): shape = tf.TensorShape(input_shape).as_list() shape[-1] = self.output_dim return tf.TensorShape(shape) def get_config(self): base_config = super(MyLayer, self).get_config() base_config['output_dim'] = self.output_dim return base_config @classmethod def from_config(cls, config): return cls(**config) model = tf.keras.Sequential([ MyLayer(10), layers.Activation('softmax')]) # The compile step specifies the training configuration model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), loss='categorical_crossentropy', metrics=['accuracy']) # Trains for 5 epochs. model.fit(data, labels, batch_size=32, epochs=5)