def tsinalis(input_shape, n_classes):
"""
Input size should be [batch, 1d, 2d, ch] = (None, 1, 15000, 1)
"""
model = Sequential(name='Tsinalis')
model.add(Conv1D (kernel_size = (200), filters = 20, input_shape=input_shape, activation='relu'))
print(model.input_shape)
print(model.output_shape)
model.add(MaxPooling1D(pool_size = (20), strides=(10)))
print(model.output_shape)
model.add(keras.layers.core.Reshape([20,-1,1]))
print(model.output_shape)
model.add(Conv2D (kernel_size = (20,30), filters = 400, activation='relu'))
print(model.output_shape)
model.add(MaxPooling2D(pool_size = (1,10), strides=(1,2)))
print(model.output_shape)
model.add(Flatten())
print(model.output_shape)
model.add(Dense (500, activation='relu'))
model.add(Dense (500, activation='relu'))
model.add(Dense(n_classes, activation = 'softmax',activity_regularizer=keras.regularizers.l2() ))
model.compile( loss='categorical_crossentropy', optimizer=keras.optimizers.SGD(), metrics=[keras.metrics.categorical_accuracy])
return model
python类MaxPooling1D()的实例源码
def first_block(tensor_input,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = Conv1D(k1,1,padding='same')(tensor_input)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv1D(k2,kernel_size,padding='same')(out)
pooling = MaxPooling1D(pooling_size,padding='same')(tensor_input)
# out = merge([out,pooling],mode='sum')
out = add([out,pooling])
return out
def rcnn(input_shape, n_classes):
"""
Input size should be [batch, 1d, ch] = (XXX, 3000, 1)
"""
model = Sequential(name='RCNN test')
model.add(Conv1D (kernel_size = (200), filters = 20, batch_input_shape=input_shape, activation='elu'))
model.add(MaxPooling1D(pool_size = (20), strides=(10)))
model.add(Conv1D (kernel_size = (20), filters = 200, activation='elu'))
model.add(MaxPooling1D(pool_size = (10), strides=(3)))
model.add(Conv1D (kernel_size = (20), filters = 200, activation='elu'))
model.add(MaxPooling1D(pool_size = (10), strides=(3)))
model.add(Dense (512, activation='elu'))
model.add(Dense (512, activation='elu'))
model.add(Reshape((1,model.output_shape[1])))
model.add(LSTM(256, stateful=True, return_sequences=False))
model.add(Dropout(0.3))
model.add(Dense(n_classes, activation = 'sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer=Adadelta())
return model
def repeated_block(x,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = BatchNormalization()(x)
out = Activation('relu')(out)
out = Conv1D(k1,kernel_size,strides=2,padding='same')(out)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv1D(k2,kernel_size,strides=2,padding='same')(out)
pooling = MaxPooling1D(pooling_size,strides=4,padding='same')(x)
out = add([out, pooling])
#out = merge([out,pooling])
return out
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(self.l1_decay, self.l2_decay)
x = kl.Conv1D(128, 11,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(4)(x)
x = kl.Flatten()(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Dense(self.nb_hidden,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(128, 11,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(4)(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(256, 7,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(4)(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
gru = kl.recurrent.GRU(256, kernel_regularizer=kernel_regularizer)
x = kl.Bidirectional(gru)(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
kaggleQQCharCNNPlus.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def createBaseNetworkSmall(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Embedding(input_dim=inputDim,
output_dim=inputDim, input_length=inputLength))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
kaggleQQCharCNNPlus.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def createBaseNetworkLarge(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Embedding(input_dim=inputDim,
output_dim=inputDim, input_length=inputLength))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
kaggleQQDistRMS_CL.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def createBaseNetworkSmall(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Embedding(input_dim=inputDim,
output_dim=inputDim, input_length=inputLength))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
kaggleQQDistRMS_CL.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def createBaseNetworkLarge(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Embedding(input_dim=inputDim,
output_dim=inputDim, input_length=inputLength))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
kaggleQQSigmoid_SG_smallerAlphabet.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def createBaseNetworkSmall(inputLength, inputDim):
baseNetwork = Sequential()
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', input_shape=(inputLength, inputDim),
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu',
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu',
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu',
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu',
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu',
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
kaggleQQSigmoid_SG_BCE.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def createBaseNetworkSmall(inputLength, inputDim):
baseNetwork = Sequential()
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', input_shape=(inputLength, inputDim),
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
kaggleQQSigmoid_SG_BCE.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def createBaseNetworkLarge(inputLength, inputDim):
baseNetwork = Sequential()
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', input_shape=(inputLength, inputDim),
kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
def createBaseNetworkSmall(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Embedding(input_dim=inputDim,
output_dim=inputDim, input_length=inputLength))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
def createBaseNetworkLarge(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Embedding(input_dim=inputDim,
output_dim=inputDim, input_length=inputLength))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
def createBaseNetworkLarge(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
testSigmoidSmaller.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def netSigmoid(inputLength, inputDim):
baseNetwork = Sequential()
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', input_shape=(inputLength, inputDim),
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
def createBaseNetworkSmall(inputLength, inputDim):
baseNetwork = Sequential()
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', input_shape=(inputLength, inputDim),
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
def createBaseNetworkSmall(inputLength, inputDim):
baseNetwork = Sequential()
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', input_shape=(inputLength, inputDim),
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(128, activation='relu'))
baseNetwork.add(Dropout(0.2))
baseNetwork.add(Dense(128, activation='relu'))
baseNetwork.add(Dropout(0.2))
return baseNetwork
def createBaseNetworkSmall(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Embedding(input_dim=inputDim, output_dim=inputDim, input_length=inputLength))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(1024, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
def createBaseNetworkLarge(inputDim, inputLength):
baseNetwork = Sequential()
baseNetwork.add(Embedding(input_dim=inputDim, output_dim=inputDim, input_length=inputLength))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(Conv1D(1024, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(mean=0.0, stddev=0.02), bias_initializer=RandomNormal(mean=0.0, stddev=0.02)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
baseNetwork.add(Dense(2048, activation='relu'))
baseNetwork.add(Dropout(0.5))
return baseNetwork
kaggleQQ_Euc_Small.py 文件源码
项目：kaggle-quora-question-pairs
作者: voletiv
项目源码
文件源码
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def createBaseNetworkSmall(inputLength, inputDim):
baseNetwork = Sequential()
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', input_shape=(inputLength, inputDim),
kernel_initializer=RandomNormal(mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 7, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Conv1D(256, 3, strides=1, padding='valid', activation='relu', kernel_initializer=RandomNormal(
mean=0.0, stddev=0.05), bias_initializer=RandomNormal(mean=0.0, stddev=0.05)))
baseNetwork.add(MaxPooling1D(pool_size=3, strides=3))
baseNetwork.add(Flatten())
baseNetwork.add(Dense(128, activation='relu'))
baseNetwork.add(Dropout(0.2))
baseNetwork.add(Dense(128, activation='relu'))
baseNetwork.add(Dropout(0.2))
return baseNetwork
def build_lstm(input_shape):
model = Sequential()
# model.add(Masking(input_shape=input_shape, mask_value=-1.))
model.add(Embedding(input_shape[0], 128, input_length=input_shape[1]))
model.add(Convolution1D(nb_filter=64,
filter_length=5,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=4))
model.add(GRU(128))
# model.add(GRU(128, return_sequences=False))
# Add dropout if overfitting
# model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def build_lstm(input_shape):
model = Sequential()
# model.add(Masking(input_shape=input_shape, mask_value=-1.))
model.add(Embedding(input_shape[0], 128, input_length=input_shape[1]))
model.add(Convolution1D(nb_filter=64,
filter_length=5,
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=model.output_shape[1]))
model.add(Flatten())
model.add(Dense(128))
# model.add(GRU(128, return_sequences=False))
# Add dropout if overfitting
# model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def _build_model(self):
# Deep Conv Neural Net for Deep-Q learning Model
model = Sequential()
model.add(Conv1D(128, 3, input_shape=(19,48)))
model.add(Activation('relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(64, 3))
model.add(Activation('relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(self.action_size))
model.add(Activation('sigmoid'))
model.compile(loss=self._huber_loss,
optimizer=Adam(lr=self.learning_rate))
#model.compile(loss='binary_crossentropy',
# optimizer='rmsprop',
# metrics=['accuracy'])
return model
def char_block(in_layer, nb_filter=(64, 100), filter_length=(3, 3), subsample=(2, 1), pool_length=(2, 2)):
block = in_layer
for i in range(len(nb_filter)):
block = Conv1D(filters=nb_filter[i],
kernel_size=filter_length[i],
padding='valid',
activation='tanh',
strides=subsample[i])(block)
# block = BatchNormalization()(block)
# block = Dropout(0.1)(block)
if pool_length[i]:
block = MaxPooling1D(pool_size=pool_length[i])(block)
# block = Lambda(max_1d, output_shape=(nb_filter[-1],))(block)
block = GlobalMaxPool1D()(block)
block = Dense(128, activation='relu')(block)
return block
def cnn3adam_slim(input_shape, n_classes):
"""
Input size should be [batch, 1d, 2d, ch] = (None, 3000, 3)
"""
model = Sequential(name='cnn3adam')
model.add(Conv1D (kernel_size = (50), filters = 32, strides=5, input_shape=input_shape, kernel_initializer='he_normal', activation='elu'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Conv1D (kernel_size = (5), filters = 64, strides=1, kernel_initializer='he_normal', activation='elu'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(MaxPooling1D())
model.add(Conv1D (kernel_size = (5), filters = 64, strides=2, kernel_initializer='he_normal', activation='elu'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(MaxPooling1D())
model.add(Flatten())
model.add(Dense (250, activation='elu', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense (250, activation='elu', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation = 'softmax'))
model.compile(loss='categorical_crossentropy', optimizer=Adam())
return model
def cnn3adam_filter(input_shape, n_classes):
"""
Input size should be [batch, 1d, 2d, ch] = (None, 3000, 3)
"""
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
print('use L2 model instead!')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
model = Sequential(name='cnn3adam_filter')
model.add(Conv1D (kernel_size = (50), filters = 128, strides=5, input_shape=input_shape, kernel_initializer='he_normal', activation='elu'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Conv1D (kernel_size = (5), filters = 256, strides=1, kernel_initializer='he_normal', activation='elu'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(MaxPooling1D())
model.add(Conv1D (kernel_size = (5), filters = 300, strides=2, kernel_initializer='he_normal', activation='elu'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(MaxPooling1D())
model.add(Flatten(name='conv3'))
model.add(Dense (1500, activation='elu', kernel_initializer='he_normal'))
model.add(BatchNormalization(name='fc1'))
model.add(Dropout(0.5))
model.add(Dense (1500, activation='elu', kernel_initializer='he_normal'))
model.add(BatchNormalization(name='fc2'))
model.add(Dropout(0.5))
model.add(Dense(n_classes, activation = 'softmax',name='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001))
return model
def cnn3adam_filter_l2(input_shape, n_classes):
"""
Input size should be [batch, 1d, 2d, ch] = (None, 3000, 3)
"""
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
print('use more L2 model instead!')
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
model = Sequential(name='cnn3adam_filter_l2')
model.add(Conv1D (kernel_size = (50), filters = 128, strides=5, input_shape=input_shape,
kernel_initializer='he_normal', activation='relu',kernel_regularizer=keras.regularizers.l2(0.005)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Conv1D (kernel_size = (5), filters = 256, strides=1, kernel_initializer='he_normal', activation='relu',kernel_regularizer=keras.regularizers.l2(0.005)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(MaxPooling1D())
model.add(Conv1D (kernel_size = (5), filters = 300, strides=2, kernel_initializer='he_normal', activation='relu',kernel_regularizer=keras.regularizers.l2(0.005)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(MaxPooling1D())
model.add(Flatten(name='conv3'))
model.add(Dense (1500, activation='relu', kernel_initializer='he_normal',name='fc1'))
model.add(BatchNormalization(name='bn1'))
model.add(Dropout(0.5, name='do1'))
model.add(Dense (1500, activation='relu', kernel_initializer='he_normal',name='fc2'))
model.add(BatchNormalization(name='bn2'))
model.add(Dropout(0.5, name='do2'))
model.add(Dense(n_classes, activation = 'softmax',name='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001))
# print('reset learning rate')
return model
def cnn3adam_filter_morel2_slim(input_shape, n_classes):
"""
Input size should be [batch, 1d, 2d, ch] = (None, 3000, 3)
"""
model = Sequential(name='cnn3adam_filter_morel2_slim')
model.add(Conv1D (kernel_size = (50), filters = 128, strides=5, input_shape=input_shape,
kernel_initializer='he_normal', activation='relu',kernel_regularizer=keras.regularizers.l2(0.05)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Conv1D (kernel_size = (5), filters = 128, strides=1, kernel_initializer='he_normal', activation='relu',kernel_regularizer=keras.regularizers.l2(0.01)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(MaxPooling1D())
model.add(Conv1D (kernel_size = (5), filters = 256, strides=2, kernel_initializer='he_normal', activation='relu',kernel_regularizer=keras.regularizers.l2(0.01)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(MaxPooling1D())
model.add(Flatten(name='conv3'))
model.add(Dense (512, activation='relu', kernel_initializer='he_normal',name='fc1'))
model.add(BatchNormalization(name='bn1'))
model.add(Dropout(0.5, name='do1'))
model.add(Dense (512, activation='relu', kernel_initializer='he_normal',name='fc2'))
model.add(BatchNormalization(name='bn2'))
model.add(Dropout(0.5, name='do2'))
model.add(Dense(n_classes, activation = 'softmax',name='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001))
# print('reset learning rate')
return model
