build model

def build_model(width, num_channels):
    width, height, n_len, n_class = width, 40, 7, len(chars)+ 1
    rnn_size = 256
    input_tensor = Input((width, 40, 3))
    x = input_tensor
    base_conv = 32
    for i in range(3):
        x = Conv2D(base_conv * (2 ** (i)), (3, 3))(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = MaxPooling2D(pool_size=(2, 2))(x)
    conv_shape = x.get_shape()
    x = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2] * conv_shape[3])))(x)
    x = Dense(32)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(x)
    gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(x)
    gru1_merged = add([gru_1, gru_1b])
    gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
    gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
    x = concatenate([gru_2, gru_2b])
    x = Dropout(0.25)(x)
    x = Dense(n_class, kernel_initializer='he_normal', activation='softmax')(x)
    y_pred = x
    return input_tensor, y_pred