def create_fel_res():
inp = Input(getInputDim())
x = Conv2D(32, (3, 3), activation='relu')(inp)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((3, 3), strides=2)(x)
x = create_highway(x, 64, 1)
x = create_highway(x, 64, 1)
x = MaxPooling2D((3, 3), strides=2)(x)
x = create_highway(x, 64, 2)
x = MaxPooling2D((3, 3), strides=2)(x)
x = Conv2D(32, (1, 1), activation='relu')(x)
# x = GlobalAveragePooling2D()(x)
# x = Activation('softmax')(x)
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
x = Dense(512, activation='sigmoid')(x)
x = Dense(512, activation='sigmoid')(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(inp, x)
model.compile(loss='binary_crossentropy',
optimizer='adagrad',
metrics=[metrics.mitos_fscore, 'binary_accuracy'])
return model
def create_simple_model():
model = Sequential()
model.add(
Conv2D(32, (3, 3),
input_shape=getInputDim(),
activation='relu',
padding='same'))
# model.add((Conv2D(32, (3,3), activation='relu', padding='same')))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D((3, 3), strides=(2, 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(0.7))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adagrad',
metrics=[metrics.mitos_fscore, 'binary_accuracy'])
return model
def create_vgg16():
input_shape = getInputDim()
img_input = Input(shape=input_shape)
x = Conv2D(64, (3, 3), activation='relu', padding='valid', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='valid', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(512, activation='relu', name='fc1')(x)
x = Dense(512, activation='relu', name='fc2')(x)
x = Dense(2, activation='softmax', name='predictions')(x)
# Create model.
model = Model(img_input, x, name='vgg16')
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=[metrics.mitos_fscore])
return model
def create_squeeze_net():
inp = Input(shape=getInputDim())
x = Conv2D(64, (3,3), padding='same', activation='relu')(inp)
x = MaxPooling2D((3,3), strides=2)(x)
x = create_fire_mod(x, 64, 128)
x = MaxPooling2D((3,3), strides=2)(x)
x = create_fire_mod(x, 64, 128)
x = MaxPooling2D((3,3), strides=2)(x)
x = Conv2D(32,(1,1), activation='relu')(x)
x = Flatten()(x)
x = Dense(64, activation='relu')(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(inp, x)
model.compile(loss='binary_crossentropy',
optimizer='adagrad',
metrics=['binary_accuracy',])
return model