本文整理汇总了Python中keras.initializers.orthogonal方法的典型用法代码示例。如果您正苦于以下问题：Python initializers.orthogonal方法的具体用法？Python initializers.orthogonal怎么用？Python initializers.orthogonal使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.initializers的用法示例。
在下文中一共展示了initializers.orthogonal方法的13个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_orthogonal
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def test_orthogonal(tensor_shape):
_runner(initializers.orthogonal(), tensor_shape,
target_mean=0.) 示例2: get_model
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def get_model(embed_weights):
input_layer = Input(shape=(MAX_LEN, ), name='input')
# 1. embedding layer
# get embedding weights
print('load pre-trained embedding weights ......')
input_dim = embed_weights.shape[0]
output_dim = embed_weights.shape[1]
x = Embedding(
input_dim=input_dim,
output_dim=output_dim,
weights=[embed_weights],
trainable=False,
name='embedding'
)(input_layer)
# clean up
del embed_weights, input_dim, output_dim
gc.collect()
# 2. dropout
x = SpatialDropout1D(rate=SPATIAL_DROPOUT)(x)
# 3. bidirectional lstm
x = Bidirectional(
layer=CuDNNLSTM(RNN_UNITS, return_sequences=True,
kernel_initializer=glorot_normal(seed=1029),
recurrent_initializer=orthogonal(gain=1.0, seed=1029)),
name='bidirectional_lstm')(x)
# 4. capsule layer
capsul = Capsule(num_capsule=10, dim_capsule=10, routings=4, share_weights=True)(x) # noqa
capsul = Flatten()(capsul)
capsul = DropConnect(Dense(32, activation="relu"), prob=0.01)(capsul)
# 5. attention later
atten = Attention(step_dim=MAX_LEN, name='attention')(x)
atten = DropConnect(Dense(16, activation="relu"), prob=0.05)(atten)
x = Concatenate(axis=-1)([capsul, atten])
# 6. output (sigmoid)
output_layer = Dense(units=1, activation='sigmoid', name='output')(x)
model = Model(inputs=input_layer, outputs=output_layer)
# compile model
model.compile(loss='binary_crossentropy', optimizer='adam')
return model 示例3: _build_fn_regressor
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_regressor(input_shape):
model = Sequential(
[
Dense(100, activation="relu", input_shape=input_shape),
Dense(Integer(40, 60), activation="relu", kernel_initializer="glorot_normal"),
Dropout(Real(0.2, 0.7)),
Dense(1, activation=Categorical(["relu", "sigmoid"]), kernel_initializer="orthogonal"),
]
)
model.compile(
optimizer=Categorical(["adam", "rmsprop"]),
loss="mean_absolute_error",
metrics=["mean_absolute_error"],
)
return model 示例4: _build_fn_glorot_normal_1
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_glorot_normal_1(input_shape): # `"glorot_normal"`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer="glorot_normal"),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model
#################### `orthogonal` - Excluding default (`Initializer`) #################### 示例5: _build_fn_orthogonal_e_0
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_orthogonal_e_0(input_shape): # `orthogonal(gain=Real(0.3, 0.9))`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer=orthogonal(gain=Real(0.3, 0.9))),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model 示例6: _build_fn_orthogonal_e_2
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_orthogonal_e_2(input_shape): # `orthogonal(gain=0.5)`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer=orthogonal(gain=0.5)),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model 示例7: _build_fn_orthogonal_e_3
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_orthogonal_e_3(input_shape): # `Orthogonal(gain=0.5)`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer=Orthogonal(gain=0.5)),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model
#################### `orthogonal` - Including default (`Initializer`) #################### 示例8: _build_fn_orthogonal_i_1
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_orthogonal_i_1(input_shape): # `orthogonal()`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer=orthogonal()),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model 示例9: _build_fn_orthogonal_i_3
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_orthogonal_i_3(input_shape): # `orthogonal(gain=1.0)`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer=orthogonal(gain=1.0)),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model 示例10: _build_fn_orthogonal_i_5
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_orthogonal_i_5(input_shape): # `orthogonal(gain=Real(0.6, 1.6))`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer=orthogonal(gain=Real(0.6, 1.6))),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model 示例11: _build_fn_categorical_0
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_categorical_0(input_shape): # `Categorical(["glorot_normal", "orthogonal"])`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer=Categorical(["glorot_normal", "orthogonal"])),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model 示例12: _build_fn_categorical_1
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def _build_fn_categorical_1(input_shape): # `Categorical([glorot_normal(), orthogonal()])`
model = Sequential(
[
Dense(Integer(50, 100), input_shape=input_shape),
Dense(1, kernel_initializer=Categorical([glorot_normal(), orthogonal()])),
]
)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
return model 示例13: test_in_custom_arg_callable
# 需要导入模块: from keras import initializers [as 别名]
# 或者: from keras.initializers import orthogonal [as 别名]
def test_in_custom_arg_callable(self, old_opt, new_opt):
assert in_similar_experiment_ids(old_opt, new_opt)
##################################################
# `orthogonal` - Including default (`Initializer`)
##################################################