CODEWITHSUNDEEP
pythontensorflow
user2711115
user2711115

Reputation: 457

Adapting Tensorflow beginner tutorial to own dataset

I'm trying to adapt the Tensorflow MNIST beginner tutorial to my own set of images. Here's my code so far: 

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""A very simple MNIST classifier.

See extensive documentation at
http://tensorflow.org/tutorials/mnist/beginners/index.md
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# Import data
# from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf
import pandas as pd
import numpy as np

IMAGE_WIDTH = 240
IMAGE_HEIGHT = 240
IMAGE_SIZE = IMAGE_WIDTH * IMAGE_HEIGHT
SYMBOLS = ["accident", "bomb", "car", "casualty", "electricity", "fire", "firebrigade", "flood", "gaz", "injury", "paramedics", "person", "police", "roadblock"]
NUM_CLASSES = len(SYMBOLS)

def chunks(l, n):
  n = max(1, n)
  return (l[i:i+n] for i in xrange(0, len(l), n))

def main(_):
  # Create the model
  x = tf.placeholder(tf.float32, [None, IMAGE_SIZE])
  W = tf.Variable(tf.zeros([IMAGE_SIZE, NUM_CLASSES]))
  b = tf.Variable(tf.zeros([NUM_CLASSES]))
  y = tf.matmul(x, W) + b

  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES])

  # The raw formulation of cross-entropy,
  #
  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
  #                                 reduction_indices=[1]))
  #
  # can be numerically unstable.
  #
  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
  # outputs of 'y', and then average across the batch.
  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

  sess = tf.InteractiveSession()

  train = pd.read_csv('test.csv')
  train.head()

  xs = []
  for img_name in train.filename:
    img = tf.image.decode_png(img_name)
    img = tf.image.rgb_to_grayscale(img)
    img = tf.image.resize_images(img, [IMAGE_WIDTH, IMAGE_HEIGHT])
    img = tf.expand_dims(img, 0)
    xs.append(img)

  ys = []
  for label_name in train.label:
    one_hot = [0] * NUM_CLASSES
    one_hot[int(label_name)] = 1
    ys.append(one_hot)

  # Train
  tf.initialize_all_variables().run()

  batches_img = [xs[x:x+100] for x in xrange(0, len(xs), 100)]
  batches_labels = [ys[x:x+100] for x in xrange(0, len(ys), 100)]

  for i, e in enumerate(batches_img):
    sess.run(train_step, feed_dict={x: batches_img[i], y_: batches_labels[i]})

  print("Training done!\n")

  # Test trained model
  """test = pd.read_csv('test.csv')
  test.head()

  xs_test = []
  for img_name in test.filename:
    img = imread(img_name, flatten=True)
    xs_test.append(img)

  ys_test = []
  for label_name in test.label:
    one_hot = [0] * NUM_CLASSES
    one_hot[int(label_name)] = 1
    ys_test.append(one_hot)

  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  print(sess.run(accuracy, feed_dict={x: xs_test, y_: ys_test}))"""

if __name__ == '__main__':
  tf.app.run()

And the error I get is

TypeError: Cannot interpret feed_dict key as Tensor: Can not convert a int into a Tensor.

Upvotes: 1

Views: 164

Answers (1)

Abhinav Upadhyay
Abhinav Upadhyay

Reputation: 2585

The feed_dict parameter expects tensors as its values, whereas you are passing an individual item from your batches_img and batches_labels lists. if you see the following line:

for i, e in enumerate(batches_img):
    sess.run(train_step, feed_dict={x: batches_img[i], y_: batches_labels[i]})

batches_img[i] will give you an individual element from the batches_img list, similarly batches_labels[i] will get you one item from the batches_label list, what you need to do is to pass a list (or np array, or a tf.Tensor object).

Upvotes: 0

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