Pragyan93 - 1 year ago 416

Python Question

I am new to TensorFlow and machine learning. I am trying to classify two objects a cup and a pendrive (jpeg images). I have trained and exported a model.ckpt successfully. Now I am trying to restore the saved model.ckpt for prediction. Here is the script:

`import tensorflow as tf`

import math

import numpy as np

from PIL import Image

from numpy import array

# image parameters

IMAGE_SIZE = 64

IMAGE_CHANNELS = 3

NUM_CLASSES = 2

def main():

image = np.zeros((64, 64, 3))

img = Image.open('./IMG_0849.JPG')

img = img.resize((64, 64))

image = array(img).reshape(64,64,3)

k = int(math.ceil(IMAGE_SIZE / 2.0 / 2.0 / 2.0 / 2.0))

# Store weights for our convolution and fully-connected layers

with tf.name_scope('weights'):

weights = {

# 5x5 conv, 3 input channel, 32 outputs each

'wc1': tf.Variable(tf.random_normal([5, 5, 1 * IMAGE_CHANNELS, 32])),

# 5x5 conv, 32 inputs, 64 outputs

'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),

# 5x5 conv, 64 inputs, 128 outputs

'wc3': tf.Variable(tf.random_normal([5, 5, 64, 128])),

# 5x5 conv, 128 inputs, 256 outputs

'wc4': tf.Variable(tf.random_normal([5, 5, 128, 256])),

# fully connected, k * k * 256 inputs, 1024 outputs

'wd1': tf.Variable(tf.random_normal([k * k * 256, 1024])),

# 1024 inputs, 2 class labels (prediction)

'out': tf.Variable(tf.random_normal([1024, NUM_CLASSES]))

}

# Store biases for our convolution and fully-connected layers

with tf.name_scope('biases'):

biases = {

'bc1': tf.Variable(tf.random_normal([32])),

'bc2': tf.Variable(tf.random_normal([64])),

'bc3': tf.Variable(tf.random_normal([128])),

'bc4': tf.Variable(tf.random_normal([256])),

'bd1': tf.Variable(tf.random_normal([1024])),

'out': tf.Variable(tf.random_normal([NUM_CLASSES]))

}

saver = tf.train.Saver()

with tf.Session() as sess:

saver.restore(sess, "./model.ckpt")

print "...Model Loaded..."

x_ = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE , IMAGE_SIZE , IMAGE_CHANNELS])

y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES])

keep_prob = tf.placeholder(tf.float32)

init = tf.initialize_all_variables()

sess.run(init)

my_classification = sess.run(tf.argmax(y_, 1), feed_dict={x_:image})

print 'Neural Network predicted', my_classification[0], "for your image"

if __name__ == '__main__':

main()

When I run the above script for prediction I get the following error:

ValueError: Cannot feed value of shape (64, 64, 3) for Tensor u'Placeholder:0', which has shape '(?, 64, 64, 3)' . What am I doing wrong? And how do I fix the shape of numpy array?

Thanks in advance

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

`image`

has a shape of `(64,64,3)`

.

Your input placeholder `_x`

have a shape of `(?, 64,64,3)`

.

The problem is that you're feeding the placeholder with a value of a different shape.

You have to feed it with a value of `(1, 64, 64, 3)`

= a batch of 1 image.

Just reshape your `image`

value to a batch with size one.

```
image = array(img).reshape(1, 64,64,3)
```

P.S: the fact that the input placeholder accepts a batch of images, means that you can run predicions for a batch of images in parallel.
You can try to read more than 1 image (N images) and than build a batch of N image, using a tensor with shape `(N, 64,64,3)`

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**