Python keras.layers.advanced_activations.PReLU() Examples
The following are 30
code examples of keras.layers.advanced_activations.PReLU().
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Example #1
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=100, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.1))
model.add(Dense(input_dim=100,output_dim=300, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.6))
model.add(Dense(input_dim=300,output_dim=50, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.6))
model.add(Dense(input_dim=50,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #2
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=310, init='he_normal'))
model.add(LeakyReLU(alpha=.001))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=310,output_dim=252, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(input_dim=252,output_dim=128, init='he_normal'))
model.add(LeakyReLU(alpha=.001))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Dense(input_dim=128,output_dim=2, init='he_normal', activation='softmax'))
#model.add(Activation('softmax'))
sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #3
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=62, init='he_normal'))
model.add(LeakyReLU(alpha=.001))
model.add(Dropout(0.3))
model.add(Dense(input_dim=62,output_dim=158, init='he_normal'))
model.add(LeakyReLU(alpha=.001))
model.add(Dropout(0.25))
model.add(Dense(input_dim=158,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
#model.add(Activation('softmax'))
sgd = SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #4
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=100, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.2))
model.add(Dense(input_dim=100,output_dim=380, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.6))
model.add(Dense(input_dim=380,output_dim=50, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.6))
model.add(Dense(input_dim=50,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #5
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=105, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=105,output_dim=280, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=280,output_dim=60, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=60,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.99, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #6
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.2, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=100, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=100,output_dim=180, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=180,output_dim=50, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(input_dim=50,output_dim=30, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=30,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #7
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=100, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=100,output_dim=360, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=360,output_dim=50, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=50,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #8
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=110, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=110,output_dim=350, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=350,output_dim=50, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=50,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #9
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=110, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(input_dim=110,output_dim=300, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(input_dim=300,output_dim=60, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=60,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #10
| Source File: model_zoo.py From visual_turing_test-tutorial with MIT License | 6 votes |
|
def deep_mlp(self):
"""
Deep Multilayer Perceptrop.
"""
if self._config.num_mlp_layers == 0:
self.add(Dropout(0.5))
else:
for j in xrange(self._config.num_mlp_layers):
self.add(Dense(self._config.mlp_hidden_dim))
if self._config.mlp_activation == 'elu':
self.add(ELU())
elif self._config.mlp_activation == 'leaky_relu':
self.add(LeakyReLU())
elif self._config.mlp_activation == 'prelu':
self.add(PReLU())
else:
self.add(Activation(self._config.mlp_activation))
self.add(Dropout(0.5))
Example #11
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=105, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=105,output_dim=200, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=200,output_dim=60, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(input_dim=60,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.99, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #12
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=140, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=140,output_dim=380, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=380,output_dim=50, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=50,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #13
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=100, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=100,output_dim=360, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=360,output_dim=50, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=50,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.007, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #14
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=110, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=110,output_dim=350, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=350,output_dim=150, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=150,output_dim=20, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(input_dim=20,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.02, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #15
| Source File: ikki_NN_1.py From stacking with MIT License | 6 votes |
|
def build_model(self):
model = Sequential()
model.add(Dropout(0.1, input_shape=(nn_input_dim_NN,)))
model.add(Dense(input_dim=nn_input_dim_NN, output_dim=110, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(input_dim=110,output_dim=200, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(input_dim=200,output_dim=60, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(input_dim=60,output_dim=80, init='he_normal'))
model.add(PReLU(init='zero'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(input_dim=80,output_dim=2, init='he_normal', activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')
return KerasClassifier(nn=model,**self.params)
Example #16
| Source File: mtcnn_model.py From SmooFaceEngine with Apache License 2.0 | 6 votes |
|
def create_Kao_Onet( weight_path = 'model48.h5'):
input = Input(shape = [48,48,3])
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1,2],name='prelu1')(x)
x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1,2],name='prelu2')(x)
x = MaxPool2D(pool_size=3, strides=2)(x)
x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1,2],name='prelu3')(x)
x = MaxPool2D(pool_size=2)(x)
x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
x = PReLU(shared_axes=[1,2],name='prelu4')(x)
x = Permute((3,2,1))(x)
x = Flatten()(x)
x = Dense(256, name='conv5') (x)
x = PReLU(name='prelu5')(x)
classifier = Dense(2, activation='softmax',name='conv6-1')(x)
bbox_regress = Dense(4,name='conv6-2')(x)
landmark_regress = Dense(10,name='conv6-3')(x)
model = Model([input], [classifier, bbox_regress, landmark_regress])
model.load_weights(weight_path, by_name=True)
return model
Example #17
| Source File: mtcnn_model.py From SmooFaceEngine with Apache License 2.0 | 6 votes |
|
def create_Kao_Rnet (weight_path = 'model24.h5'):
input = Input(shape=[24, 24, 3]) # change this shape to [None,None,3] to enable arbitraty shape input
x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)
x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
x = MaxPool2D(pool_size=3, strides=2)(x)
x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
x = Permute((3, 2, 1))(x)
x = Flatten()(x)
x = Dense(128, name='conv4')(x)
x = PReLU( name='prelu4')(x)
classifier = Dense(2, activation='softmax', name='conv5-1')(x)
bbox_regress = Dense(4, name='conv5-2')(x)
model = Model([input], [classifier, bbox_regress])
model.load_weights(weight_path, by_name=True)
return model
Example #18
| Source File: mtcnn.py From mtcnn-keras with MIT License | 6 votes |
|
def create_Pnet(weight_path):
input = Input(shape=[None, None, 3])
x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1,2],name='PReLU1')(x)
x = MaxPool2D(pool_size=2)(x)
x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1,2],name='PReLU2')(x)
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1,2],name='PReLU3')(x)
classifier = Conv2D(2, (1, 1), activation='softmax', name='conv4-1')(x)
# 无激活函数，线性。
bbox_regress = Conv2D(4, (1, 1), name='conv4-2')(x)
model = Model([input], [classifier, bbox_regress])
model.load_weights(weight_path, by_name=True)
return model
#-----------------------------#
# mtcnn的第二段
# 精修框
#-----------------------------#
Example #19
| Source File: encoder.py From enet-keras with MIT License | 6 votes |
|
def build(inp, dropout_rate=0.01):
enet = initial_block(inp)
enet = BatchNormalization(momentum=0.1)(enet) # enet_unpooling uses momentum of 0.1, keras default is 0.99
enet = PReLU(shared_axes=[1, 2])(enet)
enet = bottleneck(enet, 64, downsample=True, dropout_rate=dropout_rate) # bottleneck 1.0
for _ in range(4):
enet = bottleneck(enet, 64, dropout_rate=dropout_rate) # bottleneck 1.i
enet = bottleneck(enet, 128, downsample=True) # bottleneck 2.0
# bottleneck 2.x and 3.x
for _ in range(2):
enet = bottleneck(enet, 128) # bottleneck 2.1
enet = bottleneck(enet, 128, dilated=2) # bottleneck 2.2
enet = bottleneck(enet, 128, asymmetric=5) # bottleneck 2.3
enet = bottleneck(enet, 128, dilated=4) # bottleneck 2.4
enet = bottleneck(enet, 128) # bottleneck 2.5
enet = bottleneck(enet, 128, dilated=8) # bottleneck 2.6
enet = bottleneck(enet, 128, asymmetric=5) # bottleneck 2.7
enet = bottleneck(enet, 128, dilated=16) # bottleneck 2.8
return enet
Example #20
| Source File: encoder.py From enet-keras with MIT License | 6 votes |
|
def build(inp, dropout_rate=0.01):
pooling_indices = []
enet, indices_single = initial_block(inp)
enet = BatchNormalization(momentum=0.1)(enet) # enet_unpooling uses momentum of 0.1, keras default is 0.99
enet = PReLU(shared_axes=[1, 2])(enet)
pooling_indices.append(indices_single)
enet, indices_single = bottleneck(enet, 64, downsample=True, dropout_rate=dropout_rate) # bottleneck 1.0
pooling_indices.append(indices_single)
for _ in range(4):
enet = bottleneck(enet, 64, dropout_rate=dropout_rate) # bottleneck 1.i
enet, indices_single = bottleneck(enet, 128, downsample=True) # bottleneck 2.0
pooling_indices.append(indices_single)
# bottleneck 2.x and 3.x
for _ in range(2):
enet = bottleneck(enet, 128) # bottleneck 2.1
enet = bottleneck(enet, 128, dilated=2) # bottleneck 2.2
enet = bottleneck(enet, 128, asymmetric=5) # bottleneck 2.3
enet = bottleneck(enet, 128, dilated=4) # bottleneck 2.4
enet = bottleneck(enet, 128) # bottleneck 2.5
enet = bottleneck(enet, 128, dilated=8) # bottleneck 2.6
enet = bottleneck(enet, 128, asymmetric=5) # bottleneck 2.7
enet = bottleneck(enet, 128, dilated=16) # bottleneck 2.8
return enet, pooling_indices
Example #21
| Source File: train_predict_krs1.py From kaggler-template with GNU General Public License v3.0 | 6 votes |
|
def nn_model(dims):
model = Sequential()
model.add(Dense(400, input_dim=dims, kernel_initializer='he_normal'))
model.add(PReLU())
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Dense(200, kernel_initializer='he_normal'))
model.add(PReLU())
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(50, kernel_initializer='he_normal'))
model.add(PReLU())
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))
model.compile(loss = 'binary_crossentropy', optimizer = 'adadelta')
return(model)
Example #22
| Source File: CoarseNet_model.py From MinutiaeNet with MIT License | 6 votes |
|
def conv_bn_prelu(bottom, w_size, name, strides=(1,1), dilation_rate=(1,1)):
if dilation_rate == (1,1):
conv_type = 'conv'
else:
conv_type = 'atrousconv'
top = Conv2D(w_size[0], (w_size[1],w_size[2]),
kernel_regularizer=l2(5e-5),
padding='same',
strides=strides,
dilation_rate=dilation_rate,
name=conv_type+name)(bottom)
top = BatchNormalization(name='bn-'+name)(top)
top = PReLU(alpha_initializer='zero', shared_axes=[1,2], name='prelu-'+name)(top)
# top = Dropout(0.25)(top)
return top
Example #23
| Source File: MTCNN.py From keras-mtcnn with MIT License | 6 votes |
|
def create_Kao_Rnet (weight_path = 'model24.h5'):
input = Input(shape=[24, 24, 3]) # change this shape to [None,None,3] to enable arbitraty shape input
x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)
x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
x = MaxPool2D(pool_size=3, strides=2)(x)
x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
x = Permute((3, 2, 1))(x)
x = Flatten()(x)
x = Dense(128, name='conv4')(x)
x = PReLU( name='prelu4')(x)
classifier = Dense(2, activation='softmax', name='conv5-1')(x)
bbox_regress = Dense(4, name='conv5-2')(x)
model = Model([input], [classifier, bbox_regress])
model.load_weights(weight_path, by_name=True)
return model
Example #24
| Source File: test_keras2_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def test_tiny_conv_prelu_random(self, model_precision=_MLMODEL_FULL_PRECISION):
np.random.seed(1988)
# Define a model
from keras.layers.advanced_activations import PReLU
model = Sequential()
model.add(
Conv2D(
input_shape=(10, 10, 3), filters=3, kernel_size=(5, 5), padding="same"
)
)
model.add(PReLU(shared_axes=[1, 2]))
model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])
# Get the coreml model
self._test_model(model, model_precision=model_precision)
Example #25
| Source File: test_keras_numeric.py From coremltools with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def test_tiny_conv_prelu_random(self):
np.random.seed(1988)
# Define a model
from keras.layers.advanced_activations import PReLU
model = Sequential()
model.add(
Convolution2D(
input_shape=(10, 10, 3),
nb_filter=3,
nb_row=5,
nb_col=5,
border_mode="same",
)
)
model.add(PReLU(shared_axes=[1, 2]))
model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])
# Get the coreml model
self._test_keras_model(model)
Example #26
| Source File: co_lstm_predict_sequence.py From copper_price_forecast with GNU General Public License v3.0 | 6 votes |
|
def build_model():
"""
定义模型
"""
model = Sequential()
model.add(LSTM(units=Conf.LAYERS[1], input_shape=(Conf.LAYERS[1], Conf.LAYERS[0]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(Conf.LAYERS[2], return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=Conf.LAYERS[3]))
# model.add(BatchNormalization(weights=None, epsilon=1e-06, momentum=0.9))
model.add(Activation("tanh"))
# act = PReLU(alpha_initializer='zeros', weights=None)
# act = LeakyReLU(alpha=0.3)
# model.add(act)
start = time.time()
model.compile(loss="mse", optimizer="rmsprop")
print("> Compilation Time : ", time.time() - start)
return model
Example #27
| Source File: co_lstm_predict_day.py From copper_price_forecast with GNU General Public License v3.0 | 6 votes |
|
def build_model():
"""
定义模型
"""
model = Sequential()
model.add(LSTM(units=Conf.LAYERS[1], input_shape=(Conf.LAYERS[1], Conf.LAYERS[0]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(Conf.LAYERS[2], return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=Conf.LAYERS[3]))
# model.add(BatchNormalization(weights=None, epsilon=1e-06, momentum=0.9))
model.add(Activation("tanh"))
# act = PReLU(alpha_initializer='zeros', weights=None)
# act = LeakyReLU(alpha=0.3)
# model.add(act)
start = time.time()
model.compile(loss="mse", optimizer="rmsprop")
print("> Compilation Time : ", time.time() - start)
return model
Example #28
| Source File: MTCNN.py From keras-mtcnn with MIT License | 6 votes |
|
def create_Kao_Onet( weight_path = 'model48.h5'):
input = Input(shape = [48,48,3])
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1,2],name='prelu1')(x)
x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1,2],name='prelu2')(x)
x = MaxPool2D(pool_size=3, strides=2)(x)
x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1,2],name='prelu3')(x)
x = MaxPool2D(pool_size=2)(x)
x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
x = PReLU(shared_axes=[1,2],name='prelu4')(x)
x = Permute((3,2,1))(x)
x = Flatten()(x)
x = Dense(256, name='conv5') (x)
x = PReLU(name='prelu5')(x)
classifier = Dense(2, activation='softmax',name='conv6-1')(x)
bbox_regress = Dense(4,name='conv6-2')(x)
landmark_regress = Dense(10,name='conv6-3')(x)
model = Model([input], [classifier, bbox_regress, landmark_regress])
model.load_weights(weight_path, by_name=True)
return model
Example #29
| Source File: models.py From Hands-On-Generative-Adversarial-Networks-with-Keras with MIT License | 5 votes |
|
def build_segan_discriminator(noisy_input_shape, clean_input_shape,
n_filters=[64, 128, 256, 512, 1024],
kernel_size=(1, 31)):
clean_input = Input(shape=clean_input_shape)
noisy_input = Input(shape=noisy_input_shape)
x = Concatenate(-1)([clean_input, noisy_input])
# convolution layers
for i in range(len(n_filters)):
x = Conv2D(filters=n_filters[i], kernel_size=kernel_size,
strides=(1, 4), padding='same', use_bias=True,
kernel_initializer=weight_init)(x)
x = BatchNormalization(epsilon=1e-5, momentum=0.1)(x)
x = PReLU()(x)
x = Reshape((16384, ))(x)
# dense layers
x = Dense(256, activation=None, use_bias=True)(x)
x = PReLU()(x)
x = Dense(128, activation=None, use_bias=True)(x)
x = PReLU()(x)
x = Dense(1, activation=None, use_bias=True)(x)
# create model graph
model = Model(inputs=[noisy_input, clean_input], outputs=x, name='Discriminator')
print("\nDiscriminator")
model.summary()
return model
Example #30
| Source File: mtcnn.py From mtcnn-keras with MIT License | 5 votes |
|
def create_Onet(weight_path):
input = Input(shape = [48,48,3])
# 48,48,3 -> 23,23,32
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1,2],name='prelu1')(x)
x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
# 23,23,32 -> 10,10,64
x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1,2],name='prelu2')(x)
x = MaxPool2D(pool_size=3, strides=2)(x)
# 8,8,64 -> 4,4,64
x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1,2],name='prelu3')(x)
x = MaxPool2D(pool_size=2)(x)
# 4,4,64 -> 3,3,128
x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
x = PReLU(shared_axes=[1,2],name='prelu4')(x)
# 3,3,128 -> 128,12,12
x = Permute((3,2,1))(x)
# 1152 -> 256
x = Flatten()(x)
x = Dense(256, name='conv5') (x)
x = PReLU(name='prelu5')(x)
# 鉴别
# 256 -> 2 256 -> 4 256 -> 10
classifier = Dense(2, activation='softmax',name='conv6-1')(x)
bbox_regress = Dense(4,name='conv6-2')(x)
landmark_regress = Dense(10,name='conv6-3')(x)
model = Model([input], [classifier, bbox_regress, landmark_regress])
model.load_weights(weight_path, by_name=True)
return model
