如何导入kernel_initializer
时间: 2023-08-10 10:07:26 浏览: 51
kernel_initializer是Keras中的一个参数,用于初始化神经网络的权重。要导入kernel_initializer,可以使用以下代码:
```python
from keras import initializers
# 使用kernel_initializer初始化网络权重
model.add(Dense(64, kernel_initializer='random_normal'))
```
注意,以上代码中使用了Keras的Dense层作为示例。在实际使用时,需要根据具体情况选择不同的层类型和参数。
相关问题
tensorflow2.0导入variance_scaling_initializer
在 TensorFlow 2.0 中,可以使用以下代码导入 `variance_scaling_initializer`:
```python
from tensorflow.keras.initializers import VarianceScaling
```
然后,可以将其作为参数传递给模型的某些层或参数初始化器:
```python
model.add(Dense(10, activation='relu', kernel_initializer=VarianceScaling(scale=2.0)))
```
或者在自定义层中使用:
```python
class MyLayer(layers.Layer):
def __init__(self, units, activation=None, **kwargs):
super().__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.kernel_initializer = VarianceScaling(scale=2.0)
def build(self, input_shape):
self.kernel = self.add_weight(
shape=(input_shape[-1], self.units),
initializer=self.kernel_initializer,
name='kernel',
trainable=True)
super().build(input_shape)
def call(self, inputs):
output = tf.matmul(inputs, self.kernel)
if self.activation is not None:
output = self.activation(output)
return output
```
input1 = Input(shape=(look_back, n_inp1)) conv11 = Conv1D(filters=48, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_uniform')(input1) pool11 = MaxPool1D(pool_size=2, padding='valid')(conv11) conv12 = Conv1D(filters=48, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_uniform')(pool11) pool12 = MaxPool1D(pool_size=2, padding='valid')(conv12) gru11 = GRU(32, return_sequences=True)(pool12) # drop1 = Dropout(0.2)(gru11) gru12 = GRU(32, return_sequences=True)(gru11) time_last = tf.transpose(gru12, [0, 2, 1]) att1 = Dense(look_back, activation='tanh')(time_last) att2 = Dense(look_back, activation='softmax', use_bias=False)(att1) time_att = Multiply()([time_last, att2]) out1 = tf.reduce_sum(time_att, axis=-1)打印模型
要打印上述代码中的模型结构,可以使用Keras的`Model`类的`summary()`方法。以下是打印模型的示例代码:
```python
from keras.layers import Input, Conv1D, MaxPool1D, GRU, Dense, Multiply
from keras.models import Model
# 输入层
input1 = Input(shape=(look_back, n_inp1))
# 卷积层和池化层
conv11 = Conv1D(filters=48, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_uniform')(input1)
pool11 = MaxPool1D(pool_size=2, padding='valid')(conv11)
conv12 = Conv1D(filters=48, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_uniform')(pool11)
pool12 = MaxPool1D(pool_size=2, padding='valid')(conv12)
# GRU层
gru11 = GRU(32, return_sequences=True)(pool12)
gru12 = GRU(32, return_sequences=True)(gru11)
# 时间注意力机制
time_last = tf.transpose(gru12, [0, 2, 1])
att1 = Dense(look_back, activation='tanh')(time_last)
att2 = Dense(look_back, activation='softmax', use_bias=False)(att1)
time_att = Multiply()([time_last, att2])
out1 = tf.reduce_sum(time_att, axis=-1)
# 创建模型
model = Model(inputs=input1, outputs=out1)
# 打印模型结构
model.summary()
```
请确保在运行代码之前导入所需的库,并根据需要调整输入形状和其他层的参数。运行上述代码后,将打印出模型的详细结构信息,包括每个层的名称、输出形状和总参数数量等。
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