Rank mismatch error in Tensorflow - python-3.x

Question

I'm working on creating an image classifier that can differentiate between cats and dogs. I have the follwing code:
import cv2
import os
from tqdm import tqdm
import numpy as np
import tensorflow as tf
img_height = 128
img_width = 128
path = "./train"
# class info
file = os.listdir(path)
index = []
images = []
# image size and channels
channels = 3
n_inputs = img_width * img_height * channels
# First convolutional layer
conv1_fmaps = 96 # Number of feature maps created by this layer
conv1_ksize = 4 # kernel size 3x3
conv1_stride = 2
conv1_pad = "SAME"
# Second convolutional layer
conv2_fmaps = 192
conv2_ksize = 4
conv2_stride = 4
conv2_pad = "SAME"
# Third layer is a pooling layer
pool3_fmaps = conv2_fmaps # Isn't it obvious?
n_fc1 = 192 # Total number of output features
n_outputs = 2
with tf.name_scope("inputs"):
X = tf.placeholder(tf.float32, shape=[None, img_width, img_height, channels], name="X")
X_reshaped = tf.reshape(X, shape=[-1, img_height, img_width, channels])
y = tf.placeholder(tf.int32, shape=[None, 2], name="y")
conv1 = tf.layers.conv2d(X_reshaped, filters=conv1_fmaps, kernel_size=conv1_ksize, strides=conv1_stride, padding=conv1_pad, activation=tf.nn.relu, name="conv1")
conv2 = tf.layers.conv2d(conv1, filters=conv2_fmaps, kernel_size=conv2_ksize, strides=conv2_stride, padding=conv2_pad, activation=tf.nn.relu, name="conv2")
n_epochs = 10
batch_size = 250
with tf.name_scope("pool3"):
pool3 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
pool3_flat = tf.reshape(pool3, shape=[-1, pool3_fmaps * 8 * 8])
with tf.name_scope("fc1"):
fc1 = tf.layers.dense(pool3_flat, n_fc1, activation=tf.nn.relu name="fc1")
with tf.name_scope("output"):
logits = tf.layers.dense(fc1, n_outputs, name="output")
Y_proba = tf.nn.softmax(logits, name="Y_proba")
with tf.name_scope("train"):
xentropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y)
loss = tf.reduce_mean(xentropy)
optimizer = tf.train.AdamOptimizer()
training_op = optimizer.minimize(loss)
with tf.name_scope("eval"):
correct = tf.nn.in_top_k(logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
init = tf.global_variables_initializer()
with tf.name_scope("init_and_save"):
saver = tf.train.Saver()
def next_batch(num):
index = []
images = []
# Data set Creation
print("Creating batch dataset "+str(num+1)+"...")
for f in tqdm(range(num * batch_size, (num+1)*batch_size)):
if file[f].find("dog"):
index.append(np.array([0, 1]))
else:
index.append(np.array([1, 0]))
image = cv2.imread(path + "/" + file[f])
image = cv2.resize(image, (img_width, img_height), 0, 0, cv2.INTER_LINEAR)
# image = image.astype(np.float32)
images.append(image)
images = np.array(images, dtype=np.uint8)
images = images.astype('float32')
images = images / 255
print("\nBatch "+str(num+1)+" creation finished.")
# print([images, index])
return [images, index]
with tf.Session() as sess:
init.run()
for epoch in range(n_epochs):
for iteration in range(25000 // batch_size):
X_batch, y_batch = next_batch(iteration)
sess.run(training_op, feed_dict={X: X_batch, y: y_batch})
acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
print(epoch, "Train accuracy:", acc_train)
save_path = saver.save(sess, "./dogvscat_mnist_model.ckpt")
But I'm getting this error:
ValueError: Rank mismatch: Rank of labels (received 2) should equal rank of logits minus 1 (received 2).
Can anyone point out the problem and help me to solve it. I'm totally new to this.

Answer 1

For tf.nn.sparse_softmax_corss_entropy_with_logits rank(labels) = rank(logits) - 1, so you need to redefine the labels placeholder as follows
...
y = tf.placeholder(tf.int32, shape=[None], name="y")
...
xentropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=y)
...
X_batch, y_batch = next_batch(iteration)
y_batch = np.argmax(y_batch, axis=1)
OR you can you just use tf.nn.softmax_cross_entropy_with_logits without changing labels placeholder.
xentropy=tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y)