CODEWITHSUNDEEP
numpytensorflow
ben18785
ben18785

Reputation: 356

Tensorflow reshaping error for 1d convolutional neural net

I am building a 1d convolutional neural net for my own data (spectra) and am having an issue with tf.reshape. First I load in the data with pandas, and convert these to numpy arrays, composed of 708 training example spectra, each of length 2151,

import pandas as pd
import numpy as np
data = pd.read_csv('test.csv',header=None)
yTrue = data.ix[:,0].as_matrix()
data = data - data.mean()
data = data.ix[:,1:].as_matrix()

where I subtract the mean value in each column. So data is of dimensions 708 x 2151 here. I then create a network that starts with,

sess = tf.InteractiveSession()
## define inputs
x_ = tf.placeholder(tf.float32, shape=[None, 2151])
x_ = tf.reshape(x_, [-1,1,2151,1])
y_ = tf.placeholder(tf.float32, shape=[None])

which are inputs for my 1d convolutional neural net (with kernels with a width of 10, and 32 feature maps),

W_conv1 = weight_variable([1, 10, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

I then build the rest of the network and then try to run ADAM on it,

cost_function = tf.reduce_mean(tf.pow(y_out - y_, 2))/(2 * samples_number) #L2 loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cost_function)
correct_prediction = tf.equal(tf.argmax(y_out,1), tf.argmax(y_,1))
sess.run(tf.initialize_all_variables())
for i in range(20000):
    print(i)
    sess.run(train_step, feed_dict={x_: data, y_: yTrue})

However I get the following error:

ValueError: Cannot feed value of shape (708, 2151) for Tensor u'Reshape_26:0', 
which has shape '(?, 1, 2151, 1)'

I have looked at these answers: TensorFlow/TFLearn: ValueError: Cannot feed value of shape (64,) for Tensor u'target/Y:0', which has shape '(?, 10)'; Tensorflow error using my own data which suggest that I need to be doing some reshaping before I pass my data to the network. However, I am not sure what this should be? Particularly since the following works on the first row of the data,

t = tf.constant(data[0])
tf.reshape(t,[1,1,2151,1])

Does anyone have any ideas here?

Best,

Ben

Upvotes: 0

Views: 206

Answers (1)

Allen Lavoie
Allen Lavoie

Reputation: 5808

The issue is that feed_dict can replace any Tensor, and since you've changed x_ to reference the reshape op, that's the thing that it's trying to replace. It should work if you just use different Python variables to reference the placeholder and the reshape op:

x_placeholder_ = tf.placeholder(tf.float32, shape=[None, 2151])
x_ = tf.reshape(x_placeholder_, [-1,1,2151,1])

Then when feeding, use x_placeholder_:

sess.run(train_step, feed_dict={x_placeholder_: data, y_: yTrue})

Upvotes: 1

Related Questions