def get_training_model():
 (x, conv_layer, conv_vars) = convolutional_layers()
 W_fc1 = weight_variable([((32 * 8) * 128), 2048])
 b_fc1 = bias_variable([2048])
 conv_layer_flat = tf.reshape(conv_layer, [(-1), ((32 * 8) * 128)])
 h_fc1 = tf.nn.relu((tf.matmul(conv_layer_flat, W_fc1) + b_fc1))
 W_fc2 = weight_variable([2048, (1 + (7 * len(common.CHARS)))])
 b_fc2 = bias_variable([(1 + (7 * len(common.CHARS)))])
 y = (tf.matmul(h_fc1, W_fc2) + b_fc2)
 return (x, y, (conv_vars + [W_fc1, b_fc1, W_fc2, b_fc2]))

def get_training_model ( ) : ( x , conv_layer , conv_vars ) = convolutional_layers ( ) W_fc1 = weight_variable ( [ ( ( 32 * 8 ) * 128 ) , 2048 ] ) b_fc1 = bias_variable ( [ 2048 ] ) conv_layer_flat = tf . reshape ( conv_layer , [ ( - 1 ) , ( ( 32 * 8 ) * 128 ) ] ) h_fc1 = tf . nn . relu ( ( tf . matmul ( conv_layer_flat , W_fc1 ) + b_fc1 ) ) W_fc2 = weight_variable ( [ 2048 , ( 1 + ( 7 * len ( common . CHARS ) ) ) ] ) b_fc2 = bias_variable ( [ ( 1 + ( 7 * len ( common . CHARS ) ) ) ] ) y = ( tf . matmul ( h_fc1 , W_fc2 ) + b_fc2 ) return ( x , y , ( conv_vars + [ W_fc1 , b_fc1 , W_fc2 , b_fc2 ] ) )
the training model acts on a batch of 128x64 windows .