Training custom dataset in TensorFlow gives error - python-3.x

Question

I want to perform image classification on my custom dataset with TensorFlow. I have imported my own dataset but stuck at the training step (not sure if it imports the complete dataset or a single batch of 50 images although list contains all file names).
Dataset Info: image resolution = 88*128 (single channel), batch size = 50.
Here is the list of operations I want to perform:
Import complete dataset (change in code if it only creates a batch of 50 images)
Train the model using my own dataset (train Images and test Images)
Proper way of creating batches.
Here is the complete code, so far:
import tensorflow as tf
import os
def init_weights(shape):
init_random_dist = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(init_random_dist)
def init_bias(shape):
init_bias_vals = tf.constant(0.1, shape=shape)
return tf.Variable(init_bias_vals)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2by2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
def convolutional_layer(input_x, shape):
W = init_weights(shape)
b = init_bias([shape[3]])
return tf.nn.relu(conv2d(input_x, W) + b)
def normal_full_layer(input_layer, size):
input_size = int(input_layer.get_shape()[1])
W = init_weights([input_size, size])
b = init_bias([size])
return tf.matmul(input_layer, W) + b
def get_labels(path):
return os.listdir(path)
def files_list(path):
return [val for sublist in [[os.path.join(j) for j in i[2]] for i in os.walk(path)] for val in sublist]
def image_tensors(filesQueue):
reader = tf.WholeFileReader()
filename, content = reader.read(filesQueue)
image = tf.image.decode_jpeg(content, channels=1)
image = tf.cast(image, tf.float32)
resized_image = tf.image.resize_images(image, [88, 128])
return resized_image
path = './data/train'
trainLabels = get_labels(path)
trainingFiles = files_list(path)
trainQueue = tf.train.string_input_producer(trainingFiles)
trainBatch = tf.train.batch([image_tensors(trainQueue)], batch_size=50)
# ^^^^^^^^ a complete dataset or only a single batch? How to check?
path = './data/test'
testLabels = get_labels(path)
testingFiles = files_list(path)
testQueue = tf.train.string_input_producer(testingFiles)
testBatch = tf.train.batch([image_tensors(testQueue)], batch_size=50)
# ^^^^^^^ same here
x = tf.placeholder(tf.float32,shape=[88, 128])
y_true = tf.placeholder(tf.float32,shape=[None,len(trainLabels)])
x_image = tf.reshape(x,[-1,88,128,1])
convo_1 = convolutional_layer(x_image,shape=[6,6,1,32])
convo_1_pooling = max_pool_2by2(convo_1)
convo_2 = convolutional_layer(convo_1_pooling,shape=[6,6,32,64])
convo_2_pooling = max_pool_2by2(convo_2)
convo_2_flat = tf.reshape(convo_2_pooling,[-1,22*32*64])
full_layer_one = tf.nn.relu(normal_full_layer(convo_2_flat,1024))
hold_prob = tf.placeholder(tf.float32)
full_one_dropout = tf.nn.dropout(full_layer_one,keep_prob=hold_prob)
y_pred = normal_full_layer(full_one_dropout,10)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true,logits=y_pred))
optimizer = tf.train.AdamOptimizer(learning_rate=0.0001)
train = optimizer.minimize(cross_entropy)
init = tf.global_variables_initializer()
steps = 4000
with tf.Session() as sess:
sess.run(init)
for i in range(steps):
batch_x , batch_y = tf.train.batch(trainBatch, batch_size=50)
# ^^^^^^^^^^^ Error
sess.run(train,feed_dict={x:batch_x,y_true:batch_y,hold_prob:0.5})
if i%400 == 0:
print('Currently on step {}'.format(i))
print('Accuracy is:')
matches = tf.equal(tf.argmax(y_pred,1),tf.argmax(y_true,1))
acc = tf.reduce_mean(tf.cast(matches,tf.float32))
print(sess.run(acc,feed_dict={x:testBatch,y_true:testLabels,hold_prob:1.0}))
# ^^^^^^^^^^^^ Test Images?
print('\n')
This is the error I get:
TypeError Traceback (most recent call last)
<ipython-input-24-5d0dac5724cd> in <module>()
5 sess.run(init)
6 for i in range(steps):
----> 7 batch_x , batch_y = tf.train.batch([trainBatch], batch_size=50)
8 sess.run(train,feed_dict={x:batch_x,y_true:batch_y,hold_prob:0.5})
9
c:\users\TF_User\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py in __iter__(self)
503 TypeError: when invoked.
504 """
--> 505 raise TypeError("'Tensor' object is not iterable.")
506
507 def __bool__(self):
TypeError: 'Tensor' object is not iterable.
It seems like casting wrong type instead of Tensor or a List but can't figure out. Kindly, correct the issue and help me above listed issues.

Answer 1

It looks like you are using an unnecessary second call of tf.train.batch.
Generally you would do something like:
...
images, labels = tf.train.batch([images, labels], batch_size=50)
with tf.Session() as sess:
sess.run(init)
for i in range(steps):
sess.run(train, feed_dict={x:images,y_true:labels,hold_prob:0.5})
...
I think that TensorFlow: does tf.train.batch automatically load the next batch when the batch has finished training? should give you a better understanding of what tf.train.batch is doing and how it is used. Also the documentation on Reading Data should help too.