这篇文章主要为大家展示了如何使用keras根据层名称来初始化网络，内容简而易懂，希望大家可以学习一下，学习完之后肯定会有收获的，下面让小编带大家一起来看看吧。 keras根据层名称来初始化网络 def get_model(input_shape1=[75, 75, 3], input_shape2=[1], weights=None):
bn_model = 0
trainable = True
# kernel_regularizer = regularizers.l2(1e-4)
kernel_regularizer = None
activation = 'relu'
img_input = Input(shape=input_shape1)
angle_input = Input(shape=input_shape2)
# Block 1
x = Conv2D(64, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation=activation, padding='same',
trainable=trainable, kernel_regularizer=kernel_regularizer,
name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
branch_1 = GlobalMaxPooling2D()(x)
# branch_1 = BatchNormalization(momentum=bn_model)(branch_1)
branch_2 = GlobalAveragePooling2D()(x)
# branch_2 = BatchNormalization(momentum=bn_model)(branch_2)
branch_3 = BatchNormalization(momentum=bn_model)(angle_input)
x = (Concatenate()([branch_1, branch_2, branch_3]))
x = Dense(1024, activation=activation, kernel_regularizer=kernel_regularizer)(x)
# x = Dropout(0.5)(x)
x = Dense(1024, activation=activation, kernel_regularizer=kernel_regularizer)(x)
x = Dropout(0.6)(x)
output = Dense(1, activation='sigmoid')(x)
model = Model([img_input, angle_input], output)
optimizer = Adam(lr=1e-5, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=0.0)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
if weights is not None:
# 将by_name设置成True
model.load_weights(weights, by_name=True)
# layer_weights = h6py.File(weights, 'r')
# for idx in range(len(model.layers)):
# model.set_weights()
print 'have prepared the model.'
return model补充知识：keras.layers.Dense()方法 keras.layers.Dense()是定义网络层的基本方法，执行的操作是：output = activation（dot（input，kernel）+ bias。 其中activation是激活函数，kernel是权重矩阵，bias是偏向量。如果层输入大于2，在进行初始点积之前会将其展平。 代码如下： class Dense(Layer):
"""Just your regular densely-connected NN layer.
`Dense` implements the operation:
`output = activation(dot(input, kernel) + bias)`
where `activation` is the element-wise activation function
passed as the `activation` argument, `kernel` is a weights matrix
created by the layer, and `bias` is a bias vector created by the layer
(only applicable if `use_bias` is `True`).
Note: if the input to the layer has a rank greater than 2, then
it is flattened prior to the initial dot product with `kernel`.
# Example
```python
# as first layer in a sequential model:
model = Sequential()
model.add(Dense(32, input_shape=(16,)))
# now the model will take as input arrays of shape (*, 16)
# and output arrays of shape (*, 32)
# after the first layer, you don't need to specify
# the size of the input anymore:
model.add(Dense(32))
```
# Arguments
units: Positive integer, dimensionality of the output space.
activation: Activation function to use
(see [activations](../activations.md)).
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to
the `kernel` weights matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
nD tensor with shape: `(batch_size, ..., input_dim)`.
The most common situation would be
a 2D input with shape `(batch_size, input_dim)`.
# Output shape
nD tensor with shape: `(batch_size, ..., units)`.
For instance, for a 2D input with shape `(batch_size, input_dim)`,
the output would have shape `(batch_size, units)`.
"""
@interfaces.legacy_dense_support
def __init__(self, units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(Dense, self).__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
self.kernel = self.add_weight(shape=(input_dim, self.units),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.units,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
self.built = True
def call(self, inputs):
output = K.dot(inputs, self.kernel)
if self.use_bias:
output = K.bias_add(output, self.bias)
if self.activation is not None:
output = self.activation(output)
return output
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) >= 2
assert input_shape[-1]
output_shape = list(input_shape)
output_shape[-1] = self.units
return tuple(output_shape)
def get_config(self):
config = {
'units': self.units,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(Dense, self).get_config()
return dict(list(base_config.items()) + list(config.items()))参数说明如下： units：正整数，输出空间的维数。 activation: 激活函数。如果未指定任何内容，则不会应用任何激活函数。即“线性”激活：a（x）= x）。 use_bias：Boolean，该层是否使用偏向量。 kernel_initializer：权重矩阵的初始化方法。 bias_initializer：偏向量的初始化方法。 kernel_regularizer：权重矩阵的正则化方法。 bias_regularizer：偏向量的正则化方法。 activity_regularizer：输出层正则化方法。 kernel_constraint：权重矩阵约束函数。 bias_constraint：偏向量约束函数。 以上就是关于如何使用keras根据层名称来初始化网络的内容，如果你们有学习到知识或者技能，可以把它分享出去让更多的人看到。
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