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复现这一篇论文中的代码 loss由三部分组成，内容loss，风格loss，正则化loss，其中风格loss使用gram矩阵。Content loss
def content_loss(content_weight, content_current, content_original): """ Compute the content loss for style transfer. Inputs: - content_weight: scalar constant we multiply the content_loss by. - content_current: features of the current image, Tensor with shape [1, height, width, channels] - content_target: features of the content image, Tensor with shape [1, height, width, channels] Returns: - scalar content loss """ # tf.squared_difference(x,y,name=None) 返回的是(x-y)(x-y) return content_weight * tf.reduce_sum(tf.squared_difference(content_current,content_original))
Style loss
def gram_matrix(features, normalize=True): """ Compute the Gram matrix from features. Inputs: - features: Tensor of shape (1, H, W, C) giving features for a single image. - normalize: optional, whether to normalize the Gram matrix If True, divide the Gram matrix by the number of neurons (H * W * C) Returns: - gram: Tensor of shape (C, C) giving the (optionally normalized) Gram matrices for the input image. """ features = tf.transpose(features,[0,3,1,2]) shape = tf.shape(features) features = tf.reshape(features,(shape[0],shape[1],-1)) transpose_features = tf.transpose(features,[0,2,1]) result = tf.matmul(features,transpose_features) if normalize: result = tf.div(result,tf.cast(shape[0] * shape[1] * shape[2] * shape[3],tf.float32)) return resultdef style_loss(feats, style_layers, style_targets, style_weights): """ Computes the style loss at a set of layers. Inputs: - feats: list of the features at every layer of the current image, as produced by the extract_features function. - style_layers: List of layer indices into feats giving the layers to include in the style loss. - style_targets: List of the same length as style_layers, where style_targets[i] is a Tensor giving the Gram matrix of the source style image computed at layer style_layers[i]. - style_weights: List of the same length as style_layers, where style_weights[i] is a scalar giving the weight for the style loss at layer style_layers[i]. Returns: - style_loss: A Tensor containing the scalar style loss. """ # Hint: you can do this with one for loop over the style layers, and should # not be very much code (~5 lines). You will need to use your gram_matrix function. style_losses = 0 for i in range(len(style_layers)): cur_index = style_layers[i] cur_feat = feats[cur_index] cur_weight = style_weights[i] cur_style_target = style_targets[i] #已经是一个gram矩阵了 grammatrix = gram_matrix(cur_feat) #计算当前层的特征图的gram矩阵 style_losses += cur_weight * tf.reduce_sum(tf.squared_difference(grammatrix,cur_style_target)) return style_losses
Total-variation regularization
def tv_loss(img, tv_weight): """ Compute total variation loss. Inputs: - img: Tensor of shape (1, H, W, 3) holding an input image. - tv_weight: Scalar giving the weight w_t to use for the TV loss. Returns: - loss: Tensor holding a scalar giving the total variation loss for img weighted by tv_weight. """ # Your implementation should be vectorized and not require any loops! shape = tf.shape(img) img_row_before = tf.slice(img,[0,0,0,0],[-1,shape[1]-1,-1,-1]) img_row_after = tf.slice(img,[0,1,0,0],[-1,shape[1]-1,-1,-1]) img_col_before = tf.slice(img,[0,0,0,0],[-1,-1,shape[2]-1,-1]) img_col_after = tf.slice(img,[0,0,1,0],[-1,-1,shape[2]-1,-1]) result = tv_weight * (tf.reduce_sum(tf.squared_difference(img_row_before,img_row_after)) + tf.reduce_sum(tf.squared_difference(img_col_before,img_col_after))) return result
