DRAW Functions

# fully-conected layer
def dense(x, inputFeatures, outputFeatures, scope=None, with_w=False):
    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [inputFeatures, outputFeatures], tf.float32, tf.random_normal_initializer(stddev=0.02))
        bias = tf.get_variable("bias", [outputFeatures], initializer=tf.constant_initializer(0.0))
        if with_w:
            return tf.matmul(x, matrix) + bias, matrix, bias
        else:
            return tf.matmul(x, matrix) + bias

# merge images
def merge(images, size):
    h, w = images.shape[1], images.shape[2]
    img = np.zeros((h * size[0], w * size[1]))
    for idx, image in enumerate(images):
        i = idx % size[1]
        j = idx / size[1]
        img[j*h:j*h+h, i*w:i*w+w] = image
    return img

# save image on local machine        
def ims(name, img):
    # print img[:10][:10]
    scipy.misc.toimage(img, cmin=0, cmax=1).save(name)