Tensorflow错误：无效的参数：形状必须是一个矢量

Question

我从Kaggle尝试tensorflow与泰坦尼克号数据：https://www.kaggle.com/c/titanic

这里是我试图从Sendex实现代码：https://www.youtube.com/watch?v=PwAGxqrXSCs&index=46&list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v#t=398.046664

import tensorflow as tf 
import cleanData 
import numpy as np 

train, test = cleanData.read_and_clean() 
train = train[['Pclass', 'Sex', 'Age', 'Fare', 'Child', 'Fam_size', 'Title', 'Mother', 'Survived']] 

# one hot 
train['Died'] = int('0') 
train["Died"][train["Survived"] == 0] = 1 

print(train.head()) 

n_nodes_hl1 = 500 
n_classes = 2 
batch_size = 100 

# tf graph input 
x = tf.placeholder("float", [None, 8]) 
y = tf.placeholder("float") 

def neural_network_model(data): 

    hidden_layer_1 = {'weights':tf.Variable(tf.random_normal([8, n_nodes_hl1])), 
         'biases':tf.Variable(tf.random_normal(n_nodes_hl1))} 

    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_classes])), 
        'biases':tf.Variable(tf.random_normal([n_classes]))} 

    l1 = tf.add(tf.matmul(data, hidden_layer_1['weights']), hidden_layer_1['biases']) 
    l1 = tf.nn.relu(l1) 

    output = tf.matmul(l1, output_layer['weights']) + output_layer['biases'] 

    return output 

def train_neural_network(x): 
    prediction = neural_network_model(x) 
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction,y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 

    desired_epochs = 10 

    with tf.Session() as sess: 
     sess.run(tf.initialize_all_variables()) 

     for epoch in range(desired_epochs): 
      epoch_loss = 0 
      for _ in range(int(train.shape[0])/batch_size): 
       x_epoch, y_epoch = train.next_batch(batch_size) 
       _, c = sess.run([optimizer, cost], feed_dict= {x:x, y:y}) 
       epoch_loss += c 
      print('Epoch', epoch, 'completed out of', desired_epochs, 'loss:', epoch_loss) 

     correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1)) 
     accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
     print('Training accuracy:', accuracy.eval({x:x, y:y})) 



train_neural_network(x) 

当我运行的代码我得到一个错误，说：“W tensorflow /核心/框架/ op_kernel.cc：909]无效的参数：形状必须是{int32，int64}的向量，得到形状[]”

是有没有办法解决这个问题？我看到在Github上一个职位tensorflow的代码，显然库不采取大熊猫据帧作为输入..

Answer 1

我认为错误是在这条线：

hidden_layer_1 = {'weights': tf.Variable(tf.random_normal([8, n_nodes_hl1])), 
         'biases': tf.Variable(tf.random_normal(n_nodes_hl1))} 

的shape参数tf.random_normal()绝是整数的一维向量（或列表或数组）。对于'biases'变量，您传递一个整数，n_nodes_hl1。修复很简单，只需将该参数包装在一个列表中：

hidden_layer_1 = {..., 
         'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}