Tensorflow重塑張

Question

我有一個預測張量（實際網絡）

(Pdb) pred 
<tf.Tensor 'transpose_1:0' shape=(?, 200, 200) dtype=float32> 

和y張

y = tf.placeholder("float", [None, n_steps, n_classes]) 

(Pdb) y 
<tf.Tensor 'Placeholder_1:0' shape=(?, 200, 200) dtype=float32> 

我想將它送入

f.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))

然而，它要求尺寸爲[batch_size, num_classes]

所以我想重塑既pred和y，使他們看起來像這樣

<tf.Tensor 'transpose_1:0' shape=(?, 40000) dtype=float32> 

但是當我運行reshape我得到

(Pdb) tf.reshape(pred, [40000]) 
<tf.Tensor 'Reshape_1:0' shape=(40000,) dtype=float32> 

代替(?,40000)我怎麼能維持None尺寸？ （批量大小尺寸）

我還貼出所有相關的代碼...

# tf Graph input 
x = tf.placeholder("float", [None, n_steps, n_input]) 
y = tf.placeholder("float", [None, n_steps, n_classes]) 


# Define weights 
weights = { 
    'hidden': tf.Variable(tf.random_normal([n_hidden, n_classes]), dtype="float32"), 
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]), dtype="float32") 
} 
biases = { 
    'hidden': tf.Variable(tf.random_normal([n_hidden]), dtype="float32"), 
    'out': tf.Variable(tf.random_normal([n_classes]), dtype="float32") 
} 


def RNN(x, weights, biases): 

    # Prepare data shape to match `rnn` function requirements 
    # Current data input shape: (batch_size, n_steps, n_input) 
    # Permuting batch_size and n_steps 
    x = tf.transpose(x, [1, 0, 2]) 
    # Reshaping to (n_steps*batch_size, n_input) 

    x = tf.reshape(x, [-1, n_input]) 
    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden) 
    # This input shape is required by `rnn` function 
    x = tf.split(0, n_steps, x) 
    # Define a lstm cell with tensorflow 
    lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True) 
    outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32) 
    output_matrix = [] 

    for i in xrange(n_steps): 
     temp = tf.matmul(outputs[i], weights['out']) + biases['out'] 
     # temp = tf.matmul(weights['hidden'], outputs[i]) + biases['hidden'] 
     output_matrix.append(temp) 
    pdb.set_trace() 

    return output_matrix 

pred = RNN(x, weights, biases) 
# temp = RNN(x) 
# pdb.set_trace() 
# pred = tf.shape(temp) 
pred = tf.pack(tf.transpose(pred, [1,0,2])) 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) 

Answer 1

我在雅羅斯拉夫的評論的另一個問題的答案之一的作者。您可以將-1用於無維度。

---

你可以很容易地使用tf.reshape（）而不知道批量大小。

x = tf.placeholder(tf.float32, shape=[None, 9,2]) 
shape = x.get_shape().as_list()  # a list: [None, 9, 2] 
dim = numpy.prod(shape[1:])   # dim = prod(9,2) = 18 
x2 = tf.reshape(x, [-1, dim])   # -1 means "all" 

最後一行中的-1表示整個列，而不管運行時批處理大小是什麼。你可以在tf.reshape（）中看到它。