TensorFlow中,想要维度增加一维,可以使用tf.expand_dims(input, dim, name=None)函数。...one_img, shape=[1, one_img.get_shape()[0].value, one_img.get_shape()[1].value, 1]) 用下面的方法可以实现: one_img = tf.expand_dims...(one_img, 0) one_img = tf.expand_dims(one_img, -1) #-1表示最后一维 给出官方的例子和说明 # 't' is a tensor of shape [2
tf.expand_dims( input, axis=None, name=None, dim=None)将维数1插入张量的形状中。(弃用参数)有些论点是不赞成的。...别的例子:# 't' is a tensor of shape [2]tf.shape(tf.expand_dims(t, 0)) # [1, 2]tf.shape(tf.expand_dims(t,...1)) # [2, 1]tf.shape(tf.expand_dims(t, -1)) # [2, 1]# 't2' is a tensor of shape [2, 3, 5]tf.shape(...tf.expand_dims(t2, 0)) # [1, 2, 3, 5]tf.shape(tf.expand_dims(t2, 2)) # [2, 3, 1, 5]tf.shape(tf.expand_dims
tf.expand_dims( input, axis=None, name=None, dim=None)他所实现的功能是给定一个input,在axis轴处给input增加一个为...# 't2' is a tensor of shape [2, 3, 5]tf.shape(tf.expand_dims(t2, 0)) # [1, 2, 3, 5]因为axis=0所以矩阵的维度变成
: data[(data['A'].isin([0]))&(data['B'].isin([2]))] #isin函数 Out[15]: A B C D a 0 1 2 3 tf.expand_dims...()使用 tf.expand_dims( input, axis=None, name=None, dim=None ) 所实现的功能是给定一个input,在axis轴处给...demo: # 't2' is a tensor of shape [2, 3, 5] tf.shape(tf.expand_dims(t2, 0)) # [1, 2, 3, 5] 因为axis=0所以矩阵的维度变成
tf.expand_dims(input, axis=None, name=None, dim=None) import tensorflow as tf t = tf.ones(shape=[2...with tf.Session() as sess: print (sess.run(tf.shape(t))) print print (sess.run(tf.shape(tf.expand_dims...(t, 0)))) print print (sess.run(tf.shape(tf.expand_dims(t, 1)))) print print (sess.run...(tf.shape(tf.expand_dims(t, 2)))) print print (sess.run(tf.shape(tf.expand_dims(t, 3))))...print print (sess.run(tf.shape(tf.expand_dims(t, -1)))) [2 3 5] [1 2 3 5] [2 1 3 5] [2 3 1 5]
一般TensorFlow中扩展维度可以使用tf.expand_dims()。近来发现另一种可以直接运用取数据操作符[]就能扩展维度的方法。...操作完成之后,若要进行卷积操作,就需要对embedded的向量扩展维度,将[batch_size, embedding_dims]扩展成为[batch_size, embedding_dims, 1],利用tf.expand_dims...tf.expand_dims() tf.squeeze() tf.expand_dims() tf.expand_dims(input, axis=None, name=None, dim
# 关于数据维度的处理十分关键,因为tensorflow中卷积操作只支持四维的张量, # 所以要人为的把数据补充为4维数据[1,1,25,1] input_2d = tf.expand_dims...(input_1d, 0) input_3d = tf.expand_dims(input_2d, 0) input_4d = tf.expand_dims(input_3d, 3)...height=num_input, channels=1] # 因为在处理卷积层的结果时,使用squeeze函数对结果输出进行降维,所以此处要将最大池化层的维度提升为4维 input_2d = tf.expand_dims...(input_1d, 0) input_3d = tf.expand_dims(input_2d, 0) input_4d = tf.expand_dims(input_3d, 3)...1D input array into a 2D array for matrix multiplication # 将一维的数组添加一维成为2维数组 input_layer_2d = tf.expand_dims
8, 9]] 2.2 DIN使用 DIN之中,使用如下: scores = tf.reshape(scores, [-1, tf.shape(facts)[1]]) output = facts * tf.expand_dims...axis=0 代表第一维度,1代表第二维度,2代表第三维度,以此类推,比如: # 't2' is a tensor of shape [2, 3, 5] tf.shape(tf.expand_dims(...= [[0.1, 0.2, 0.3], [1.1, 1.2, 1.3], [2.1, 2.2, 2.3], [3.1, 3.2, 3.3], [4.1, 4.2, 4.3]] 那么 sess.run(tf.expand_dims...[0.1 0.2 0.3]] [[1.1 1.2 1.3]] [[2.1 2.2 2.3]] [[3.1 3.2 3.3]] [[4.1 4.2 4.3]] ] 而 sess.run(tf.expand_dims...# Mask # [B, T] key_masks = tf.expand_dims(mask, 1) # [B, 1, T] paddings = tf.ones_like(scores) * (-2
doing this to broadcast addition along the time axis to calculate the score query_with_time_axis = tf.expand_dims...# x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size) x = tf.concat([tf.expand_dims...enc_output, enc_hidden = encoder(inp, enc_hidden) dec_hidden = enc_hidden dec_input = tf.expand_dims..., units))] enc_out, enc_hidden = encoder(inputs, hidden) dec_hidden = enc_hidden dec_input = tf.expand_dims...result, sentence, attention_plot # the predicted ID is fed back into the model dec_input = tf.expand_dims
, 3], [4, 5, 6]]]) #扩展维度,如果想用广播特性的话,经常会用到这个函数 # 't' is a tensor of shape [2] #一次扩展一维 shape(tf.expand_dims...(t, 0)) ==> [1, 2] shape(tf.expand_dims(t, 1)) ==> [2, 1] shape(tf.expand_dims(t, -1)) ==> [2, 1] # '...t2' is a tensor of shape [2, 3, 5] shape(tf.expand_dims(t2, 0)) ==> [1, 2, 3, 5] shape(tf.expand_dims...(t2, 2)) ==> [2, 3, 1, 5] shape(tf.expand_dims(t2, 3)) ==> [2, 3, 5, 1] tf.slice() tf.slice(input_, begin
inputs.get_shape().as_list() with tf.variable_scope(scope,reuse=True): position_ind = tf.tile(tf.expand_dims...scale=True, scope="enc_embed") self.enc += embedding(tf.tile(tf.expand_dims...,而是直接把填充的部分变为0: query_masks = tf.sign(tf.abs(tf.reduce_sum(queries,axis=-1))) query_masks = tf.tile(tf.expand_dims...key_masks = tf.sign(tf.abs(tf.reduce_sum(keys,axis=-1))) key_masks = tf.tile(tf.expand_dims(key_masks...query_masks = tf.sign(tf.abs(tf.reduce_sum(queries,axis=-1))) query_masks = tf.tile(tf.expand_dims
送入状态向量,获取策略: [4] s = tf.constant(s, dtype=tf.float32) # s: [4] => [1,4] s = tf.expand_dims...送入状态向量,获取策略: [4] s = tf.constant(s, dtype=tf.float32) # s: [4] => [1,4] s = tf.expand_dims...() as tape1, tf.GradientTape() as tape2: # 取出R(st),[b,1] v_target = tf.expand_dims...pi(a|st) pi = self.actor(tf.gather(state, index, axis=0)) indices = tf.expand_dims...=1) pi_a = tf.gather_nd(pi, indices) # 动作的概率值pi(at|st), [b] pi_a = tf.expand_dims
tf.transpose(a, perm=[0, 1, 3, 2]) print(c.shape) 输出结果: (4, 3, 2, 1) (1, 2, 3, 4) (4, 3, 1, 2) tf.expand_dims...格式: tf.expand_dims( input, axis, name=None ) 参数: input: 输入- axis: 操作的维度- name: 数据名称 例子: a =...tf.random.normal([4, 3, 2, 1]) print(a.shape) b = tf.expand_dims(a, axis=0) print(b.shape) c = tf.expand_dims...(a, axis=1) print(c.shape) d = tf.expand_dims(a, axis=-1) print(d.shape) 输出结果: (4, 3, 2, 1) (1, 4
# Tensorflow的卷积操作默认输入有四个维度[batch_size, height, width, channels] # 此处我们将维度增加到四维 input_3d = tf.expand_dims...(input_2d, 0) input_4d = tf.expand_dims(input_3d, 3) convolution_output = tf.nn.conv2d(input_...# [batch_size=1, height=given, width=given, channels=1] input_3d = tf.expand_dims(input_2d,...0) input_4d = tf.expand_dims(input_3d, 3) # Perform the max pooling with strides = [1,1,1,1]...0.1) # 初始化bias bias = tf.random_normal(shape=[num_outputs]) # 将一维输入还原为二维 input_2d = tf.expand_dims
tf.keras.datasets.mnist.load_data() train_images=train_images/255 test_images=test_images/255 train_images=tf.expand_dims...(train_images,-1) test_images=tf.expand_dims(test_images,-1) train_labels=tf.cast(train_labels,tf.int64
下面给出两个样例 样例1: [1, 2, 3] == [[1],[2],[3]] import tensorflow as tf a = tf.constant([1, 2, 3]) b = tf.expand_dims...1] [2] [3]] 样例2: [1, 2, 3] == [[1,2,3]] import tensorflow as tf a = tf.constant([1, 2, 3]) b = tf.expand_dims
(xx_ones, -1) xx_range = tf.tile(tf.expand_dims(tf.range(self.x_dim), 0), [batch_size_tensor, 1]...= tf.expand_dims(xx_channel, -1) yy_ones = tf.ones([batch_size_tensor, self.y_dim], dtype=tf.int32...) yy_ones = tf.expand_dims(yy_ones, 1) yy_range = tf.tile(tf.expand_dims(tf.range(self.y_dim...), 0), [batch_size_tensor, 1]) yy_range = tf.expand_dims(yy_range, -1) yy_channel = tf.matmul...(yy_range, yy_ones) yy_channel = tf.expand_dims(yy_channel, -1) xx_channel = tf.cast(xx_channel
# x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size) x = tf.concat([tf.expand_dims...image to image hidden = decoder.reset_state(batch_size=target.shape[0]) dec_input = tf.expand_dims...i], predictions) # using teacher forcing dec_input = tf.expand_dims...max_length, attention_features_shape)) hidden = decoder.reset_state(batch_size=1) temp_input = tf.expand_dims...if index_word[predicted_id] == '': return result, attention_plot dec_input = tf.expand_dims
[] for hop in range(self.n_hop): # [batch_size, n_memory, dim, 1] h_expanded = tf.expand_dims...tf.squeeze(tf.matmul(self.r_emb_list[hop], h_expanded), axis=3) # [batch_size, dim, 1] v = tf.expand_dims...probs_normalized = tf.nn.softmax(probs) # [batch_size, n_memory, 1] probs_expanded = tf.expand_dims...logits=self.scores)) self.kge_loss = 0 for hop in range(self.n_hop): h_expanded = tf.expand_dims...(self.h_emb_list[hop], axis=2) t_expanded = tf.expand_dims(self.t_emb_list[hop], axis=3)
hidden_size) # hidden_with_time_axis shape == (batch_size, 1, hidden_size) hidden_with_time_axis = tf.expand_dims...) # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size) x = tf.concat([tf.expand_dims...image to image hidden = decoder.reset_state(batch_size=target.shape[0]) dec_input = tf.expand_dims...loss += loss_function(target[:, i], predictions) # using teacher forcing dec_input = tf.expand_dims...max_length, attention_features_shape)) hidden = decoder.reset_state(batch_size=1) temp_input = tf.expand_dims
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