張量流中的神經網絡迴歸：代碼中的錯誤

Question

我不明白爲什麼我的代碼無法運行。我從TensorFlow教程開始，使用單層前饋神經網絡對mnist數據集中的圖像進行分類。然後修改代碼以創建一個多層感知器，將37個輸入映射到1個輸出。輸入和輸出的訓練數據正在從MATLAB數據文件（.MAT）

這裏是我的代碼加載..

from __future__ import absolute_import 
from __future__ import division 
from __future__ import print_function 
from scipy.io import loadmat 
%matplotlib inline 
import tensorflow as tf 
from tensorflow.contrib import learn 

import sklearn 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 
from warnings import filterwarnings 
filterwarnings('ignore') 
sns.set_style('white') 
from sklearn import datasets 
from sklearn.preprocessing import scale 
from sklearn.cross_validation import train_test_split 
from sklearn.datasets import make_moons 

X = np.array(loadmat("Data/DataIn.mat")['TrainingDataIn']) 
Y = np.array(loadmat("Data/DataOut.mat")['TrainingDataOut']) 

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5) 
total_len = X_train.shape[0] 

# Parameters 
learning_rate = 0.001 
training_epochs = 500 
batch_size = 10 
display_step = 1 
dropout_rate = 0.9 
# Network Parameters 
n_hidden_1 = 19 # 1st layer number of features 
n_hidden_2 = 26 # 2nd layer number of features 
n_input = X_train.shape[1] 
n_classes = 1 

# tf Graph input 
X = tf.placeholder("float32", [None, 37]) 
Y = tf.placeholder("float32", [None]) 

def multilayer_perceptron(X, weights, biases): 
    # Hidden layer with RELU activation 
    layer_1 = tf.add(tf.matmul(X, weights['h1']), biases['b1']) 
    layer_1 = tf.nn.relu(layer_1) 

    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) 
    layer_2 = tf.nn.relu(layer_2) 

    # Output layer with linear activation 
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] 
    return out_layer 


# Store layers weight & bias 
weights = { 
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], 0, 0.1)), 
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], 0, 0.1)), 
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], 0, 0.1)) 
} 

biases = { 
    'b1': tf.Variable(tf.random_normal([n_hidden_1], 0, 0.1)), 
    'b2': tf.Variable(tf.random_normal([n_hidden_2], 0, 0.1)), 
    'out': tf.Variable(tf.random_normal([n_classes], 0, 0.1)) 
} 

# Construct model 
pred = multilayer_perceptron(X, weights, biases) 
tf.shape(pred) 
tf.shape(Y) 
print("Prediction matrix:", pred) 
print("Output matrix:", Y) 

# Define loss and optimizer 
cost = tf.reduce_mean(tf.square(pred-Y)) 
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 

# Launch the graph 
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 

    # Training cycle 
    for epoch in range(training_epochs): 
     avg_cost = 0. 
     total_batch = int(total_len/batch_size) 
     print(total_batch) 
     # Loop over all batches 
     for i in range(total_batch-1): 
      batch_x = X_train[i*batch_size:(i+1)*batch_size] 
      batch_y = Y_train[i*batch_size:(i+1)*batch_size] 
      # Run optimization op (backprop) and cost op (to get loss value) 
      _, c, p = sess.run([optimizer, cost, pred], feed_dict={X: batch_x, 
                  Y: batch_y}) 
      # Compute average loss 
      avg_cost += c/total_batch 

     # sample prediction 
     label_value = batch_y 
     estimate = p 
     err = label_value-estimate 
     print ("num batch:", total_batch) 

     # Display logs per epoch step 
     if epoch % display_step == 0: 
      print ("Epoch:", '%04d' % (epoch+1), "cost=", \ 
       "{:.9f}".format(avg_cost)) 
      print ("[*]----------------------------") 
      for i in xrange(5): 
       print ("label value:", label_value[i], \ 
        "estimated value:", estimate[i]) 
      print ("[*]============================") 

    print ("Optimization Finished!") 

    # Test model 
    correct_prediction = tf.equal(tf.argmax(pred), tf.argmax(Y)) 
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 
    print ("Accuracy:", accuracy.eval({X: X_test, Y: Y_test})) 

當我運行的代碼，我得到錯誤信息：

--------------------------------------------------------------------------- 
ValueError        Traceback (most recent call last) 
<ipython-input-4-6b8af9192775> in <module>() 
    93    # Run optimization op (backprop) and cost op (to get loss value) 
    94    _, c, p = sess.run([optimizer, cost, pred], feed_dict={X: batch_x, 
---> 95               Y: batch_y}) 
    96    # Compute average loss 
    97    avg_cost += c/total_batch 

~\AppData\Local\Continuum\Anaconda3\envs\ann\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata) 
    787  try: 
    788  result = self._run(None, fetches, feed_dict, options_ptr, 
--> 789       run_metadata_ptr) 
    790  if run_metadata: 
    791   proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) 

~\AppData\Local\Continuum\Anaconda3\envs\ann\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 
    973     'Cannot feed value of shape %r for Tensor %r, ' 
    974     'which has shape %r' 
--> 975     % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape()))) 
    976   if not self.graph.is_feedable(subfeed_t): 
    977    raise ValueError('Tensor %s may not be fed.' % subfeed_t) 

ValueError: Cannot feed value of shape (10, 1) for Tensor 'Placeholder_7:0', which has shape '(?,)' 

Answer 1

我以前遇到過這個問題。區別在於形狀(10, 1)的張量看起來像[[1], [2], [3]]，而形狀(10,)的張量看起來像[1, 2, 3]。

您應該能夠改變這一行

Y = tf.placeholder("float32", [None]) 

來解決它：

Y = tf.placeholder("float32", [None, 1])