Keras CNN model for character-based OCR

net.py

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, Activation
from keras import losses, optimizers, activations

# Create model
model = Sequential()

# Convolution 1 (28x28 input)
model.add(Conv2D(
    64,
    kernel_size=5,
    input_shape=(28, 28, 1),
    activation='relu'
    ))

# Max pooling 1
model.add(MaxPooling2D(
    pool_size=2
    ))

# Convolution 2
model.add(Conv2D(
    96,
    kernel_size=4,
    activation='relu'
    ))

# Max pooling 2
model.add(MaxPooling2D(
    pool_size=2
    ))

# Convolution 3
model.add(Conv2D(
    128,
    kernel_size=3,
    activation='relu'
    ))

# Max pooling 3
model.add(MaxPooling2D(
    pool_size=2
    ))

# Flatten
model.add(Flatten())

# Fully connected 1
model.add(Dense(
    512,
    activation='relu'
    ))

# Dropout
model.add(Dropout(
    0.2
))

# Fully connected 2
# (79 classifications)
model.add(Dense(
    79,
    activation='softmax'
    ))

# Compile model
model.compile(
    loss=losses.categorical_crossentropy,
    optimizer=optimizers.Adadelta(),
    metrics=['accuracy']
)