[手抄] Keras官方教程 - Concept

2018-07-06 keras, learn

官方教程，记录以便查阅！

以下代码运行环境为 —— keras[2.2.4], tensorflow[1.11.0]

models

model.layers is a flattened list of the layers comprising the model.

model.inputs is the list of input tensors of the model.

model.outputs is the list of output tensors of the model.

model.summary() prints a summary representation of your model. Shortcut for utils.print_summary

model.get_config() returns a dictionary containing the configuration of the model. The model can be reinstantiated from its config via:

config = model.get_config()
model = Model.from_config(config)
# or, for Sequential:
model = Sequential.from_config(config)

model.get_weights() returns a list of all weight tensors in the model, as Numpy arrays.

model.set_weights(weights) sets the values of the weights of the model, from a list of Numpy arrays. The arrays in the list should have the same shape as those returned by get_weights().

model.to_json() returns a representation of the model as a JSON string. Note that the representation does not include the weights, only the architecture. You can reinstantiate the same model (with reinitialized weights) from the JSON string via:

from keras.models import model_from_json

json_string = model.to_json()
model = model_from_json(json_string)

model.to_yaml() returns a representation of the model as a YAML string. Note that the representation does not include the weights, only the architecture. You can reinstantiate the same model (with reinitialized weights) from the YAML string via:

from keras.models import model_from_yaml

yaml_string = model.to_yaml()
model = model_from_yaml(yaml_string)

model.save_weights(filepath) saves the weights of the model as a HDF5 file.

model.load_weights(filepath, by_name=False) loads the weights of the model from a HDF5 file (created by save_weights). By default, the architecture is expected to be unchanged. To load weights into a different architecture (with some layers in common), use by_name=True to load only those layers with the same name.

Model subclassing example

import keras

class SimpleMLP(keras.Model):

    def __init__(self, use_bn=False, use_dp=False, num_classes=10):
        super(SimpleMLP, self).__init__(name='mlp')
        self.use_bn = use_bn
        self.use_dp = use_dp
        self.num_classes = num_classes

        self.dense1 = keras.layers.Dense(32, activation='relu')
        self.dense2 = keras.layers.Dense(num_classes, activation='softmax')
        if self.use_dp:
            self.dp = keras.layers.Dropout(0.5)
        if self.use_bn:
            self.bn = keras.layers.BatchNormalization(axis=-1)

    def call(self, inputs):
        x = self.dense1(inputs)
        if self.use_dp:
            x = self.dp(x)
        if self.use_bn:
            x = self.bn(x)
        return self.dense2(x)

model = SimpleMLP()
model.compile(...)
model.fit(...)

layers

layer.get_weights(): returns the weights of the layer as a list of Numpy arrays.

layer.set_weights(weights): sets the weights of the layer from a list of Numpy arrays (with the same shapes as the output of get_weights).

layer.get_config(): returns a dictionary containing the configuration of the layer. The layer can be reinstantiated from its config via:

layer = Dense(32)
config = layer.get_config()
reconstructed_layer = Dense.from_config(config)

Or:

from keras import layers

config = layer.get_config()
layer = layers.deserialize({'class_name': layer.__class__.__name__,
                            'config': config})

If a layer has a single node (i.e. if it isn't a shared layer), you can get its input tensor, output tensor, input shape and output shape via:

layer.input

layer.output

layer.input_shape

layer.output_shape

If the layer has multiple nodes (see: the concept of layer node and shared layers), you can use the following methods:

layer.get_input_at(node_index) layer.get_output_at(node_index) layer.get_input_shape_at(node_index) layer.get_output_shape_at(node_index)

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