TensorFlow：InvalidArgumentError：In[0] 不是矩阵

Question

I am new to TensorFlow and need to implement a deep neural network for a regression task. I assume there are no such sample codes on the internet where regression is performed using deep neural network (at least I could not find any. Please post any helpful link, if available). So, I have tried to merge the tutorials on deep neural networks for classification and regression together for my purpose. As expected, I am bombarded with errors. The error message reads:

InvalidArgumentError: In[0] is not a matrix
 [[Node: MatMul_35 = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_Placeholder_36_0, Variable_72/read)]]  

The code:

import tensorflow as tf
import numpy
import matplotlib.pyplot as plt

n_nodes_hl1 = 100
n_nodes_hl2 = 100

batch_size = 100

n_input = 1;
n_output = 1;
learning_rate = 0.01

train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                 7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                 2.827,3.465,1.65,2.904,2.42,2.94,1.3])

x = tf.placeholder('float')
y = tf.placeholder('float')

def neural_network_model(data):
   hidden_1_layer = {'weights':tf.Variable(tf.random_normal([n_input, n_nodes_hl1])),
                  'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}

   hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                  'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}

   l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']), hidden_1_layer['biases'])
   l1 = tf.nn.relu(l1)

   l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']), hidden_2_layer['biases'])
   l2 = tf.nn.relu(l2)

   output = tf.reduce_sum(l2)

   return output

def train_neural_network(x):
   prediction = neural_network_model(x)
   cost = tf.square(y - prediction)

   optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

   hm_epochs = 5
   with tf.Session() as sess:

      sess.run(tf.global_variables_initializer())

      for epoch in range(hm_epochs):
         epoch_loss = 0
         for (X, Y) in zip(train_X, train_Y):
             _, c = sess.run([optimizer, cost], feed_dict={x: X, y: Y})
             epoch_loss += c

         print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)

      plt.plot(train_X, train_Y, 'ro', label='Original data')
      plt.plot(train_X, prediction, label='Fitted line')
      plt.legend()
      plt.show()

      test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
      test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])
      print("Testing Data")

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

train_neural_network(x)

As far I guess there is an issue with the dimensions of the hidden layer weights and/or biases (I may be wrong).

Side note: Here I have just tried to make a simple model of my project where the training and testing data points have been taken from the internet examples. My actual data would be pixel values of several images.

Answer 1

Change this line (working for me) :

1. Inputs to matmul() functions should be a matrix - you are feeding a value.

    _, c = sess.run([optimizer, cost], feed_dict={x: [[X]], y: [[Y]]})

Output:

('Epoch', 0, 'completed out of', 5, 'loss:', array([[  1.20472407e+14]], dtype=float32))
('Epoch', 1, 'completed out of', 5, 'loss:', array([[ 6.82631159]], dtype=float32))
('Epoch', 2, 'completed out of', 5, 'loss:', array([[ 8.83840561]], dtype=float32))
('Epoch', 3, 'completed out of', 5, 'loss:', array([[ 8.00222397]], dtype=float32))
('Epoch', 4, 'completed out of', 5, 'loss:', array([[ 7.6564579]], dtype=float32))

Hope this helps !

Comment: This is not a good example to explore if you're going to work with images.