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models_preprocessing.py
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models_preprocessing.py
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from keras.models import Sequential
from keras.layers import Flatten, Dense, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.regularizers import l2
def compiledConvnet(input_shape=(101, 101, 4)):
model = convnet(input_shape)
optimizer = Adam(lr = .0001, decay = 5e-5)
model.compile(optimizer=optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def convnet(input_shape=(101, 101, 4)):
model = Sequential()
model.add(Conv2D(64, (3, 3), strides=(2,2), activation='softplus',
input_shape=input_shape))
model.add(Conv2D(32, (3, 3), strides=(2,2), activation='softplus'))
model.add(Conv2D(16, (3, 3), activation='softplus'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(128, activation='softplus'))
model.add(Dense(32, activation='softplus'))
model.add(Dense(1, activation='sigmoid'))
return model
def compiled_maxpool_simpler_1(input_shape=(101, 101, 4)):
model = maxpool_simpler_1(input_shape)
optimizer = Adam(lr = .0001, decay = 5e-5)
model.compile(optimizer=optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def maxpool_simpler_1(input_shape=(101, 101, 4)):
model = Sequential()
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), input_shape=input_shape))
model.add(Conv2D(32, (3, 3), strides=(2,2), activation='softplus',
input_shape=input_shape))
model.add(Conv2D(16, (3, 3), strides=(2,2), activation='softplus'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(128, activation='softplus'))
model.add(Dense(32, activation='softplus'))
model.add(Dense(1, activation='sigmoid'))
return model
def compiled_maxpool_convnet(input_shape=(101, 101, 4)):
model = convnet(input_shape)
optimizer = Adam(lr = .0001, decay = 5e-5)
model.compile(optimizer=optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
return model
def maxpool_convnet(input_shape=(101, 101, 4)):
model = Sequential()
model.add(MaxPooling2D(pool_size=(4,4), strides=(4,4), input_shape=input_shape))
model.add(Conv2D(64, (3, 3), strides=(2,2), activation='softplus'))
model.add(Conv2D(32, (3, 3), strides=(2,2), activation='softplus'))
model.add(Conv2D(16, (3, 3), activation='softplus'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(128, activation='softplus'))
model.add(Dense(32, activation='softplus'))
model.add(Dense(1, activation='sigmoid'))
return model
def compiledRegularizedConvnet(input_shape=(101, 101, 4)):
model = regularizedConvnet(input_shape)
optimizer = Adam(lr = .0001, decay = 5e-5)
model.compile(optimizer=optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
return model
reg = 0.5
def regularizedConvnet(input_shape=(101, 101, 4)):
model = Sequential()
model.add(Conv2D(64, (3, 3), strides=(2,2), activation='softplus',
input_shape=input_shape))
model.add(Conv2D(32, (3, 3), strides=(2,2), activation='softplus'))
model.add(Conv2D(16, (3, 3), activation='softplus'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(128, activation='softplus', kernel_regularizer=l2(reg)))
model.add(Dense(32, activation='softplus'))
model.add(Dense(1, activation='sigmoid'))
return model