model.py

import tensorflow as tf
from keras import backend as K
from keras.layers import Convolution2D, Flatten, Dense, Input
from keras.models import Model

def build_network(num_actions, agent_history_length, resized_width, resized_height):
  with tf.device("/cpu:0"):
    state = tf.placeholder("float", [None, agent_history_length, resized_width, resized_height])
    inputs = Input(shape=(agent_history_length, resized_width, resized_height,))
    model = Convolution2D(nb_filter=16, nb_row=8, nb_col=8, subsample=(4,4), activation='relu', border_mode='same')(inputs)
    model = Convolution2D(nb_filter=32, nb_row=4, nb_col=4, subsample=(2,2), activation='relu', border_mode='same')(model)
    model = Flatten()(model)
    model = Dense(output_dim=256, activation='relu')(model)
    q_values = Dense(output_dim=num_actions, activation='linear')(model)
    m = Model(input=inputs, output=q_values)
  return state, m