Model.py

from keras.models import Sequential
from keras.layers import Activation, Dropout, UpSampling2D, ZeroPadding2D
from keras.layers import Conv2DTranspose, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras import regularizers

def CreateModel(input_shape):
    pool_size = (2, 2)

    ### Here is the actual neural network ###
    model = Sequential()
    # Normalizes incoming inputs.  First layer needs the input shape to work
    model.add(BatchNormalization(input_shape=input_shape))

    # Below layers were re-named for easier reading of model summary; this not
    # necessary
    # Conv Layer 1
    model.add(Conv2D(32, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Conv1'))

    # Conv Layer 2
    model.add(Conv2D(64, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Conv2'))

    # Pooling 1
    model.add(MaxPooling2D(pool_size=pool_size))

    # Conv Layer 3
    model.add(Conv2D(64, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Conv3'))
    model.add(Dropout(0.2))

    # Conv Layer 4
    model.add(Conv2D(128, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Conv4'))
    model.add(Dropout(0.2))

    # Conv Layer 5
    model.add(Conv2D(128, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Conv5'))
    model.add(Dropout(0.2))

    # Pooling 2
    model.add(MaxPooling2D(pool_size=pool_size))

    # Conv Layer 6
    model.add(Conv2D(256, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Conv6'))
    model.add(Dropout(0.2))

    # Conv Layer 7
    model.add(Conv2D(256, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Conv7'))
    model.add(Dropout(0.2))

    # Pooling 3
    model.add(MaxPooling2D(pool_size=pool_size))

    # Upsample 1
    model.add(UpSampling2D(size=pool_size))

    model.add(ZeroPadding2D(padding=((0,1),(0,0))))

    # Deconv 1
    model.add(Conv2DTranspose(256, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Deconv1'))
    model.add(Dropout(0.2))

    # Deconv 2
    model.add(Conv2DTranspose(256, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Deconv2'))
    model.add(Dropout(0.2))

    # Upsample 2
    model.add(UpSampling2D(size=pool_size))

    # Deconv 3
    model.add(Conv2DTranspose(128, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Deconv3'))
    model.add(Dropout(0.2))

    # Deconv 4
    model.add(Conv2DTranspose(128, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Deconv4'))
    model.add(Dropout(0.2))

    # Deconv 5
    model.add(Conv2DTranspose(64, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Deconv5'))
    model.add(Dropout(0.2))

    # Upsample 3
    model.add(UpSampling2D(size=pool_size))

    model.add(ZeroPadding2D(padding=((2,0),(0,0))))

    # Deconv 6
    model.add(Conv2DTranspose(64, (3, 3), padding='valid', strides=(1,1), activation = 'relu', name = 'Deconv6'))

    # Final layer - only including one channel so 3 filter
    model.add(Conv2DTranspose(1, (3, 3), padding='valid', strides=(1,1), activation = 'sigmoid', name = 'Final'))

    ### End of network ###

    return model