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I am beginner to Tensorflow, and have written the following code for classification of Cats and Dogs using the Kaggle dataset. I have tinkered with the code used on IBM's edX course, and tried to create it for the Cats and Dogs dataset image classifier.

import csv

import re

import matplotlib.pyplot as plt

import datetime

import tensorflow as tf

import numpy as np

import random as rd

import os

from PIL import Image

import random





def iterate_minibatches(inputs, targets, batchsize, shuffle=False):

    assert inputs.shape[0] == targets.shape[0]

    if shuffle:

        indices = np.arange(inputs.shape[0])

        np.random.shuffle(indices)

    for start_idx in range(0, inputs.shape[0] - batchsize + 1, batchsize):

        if shuffle:

            excerpt = indices[start_idx:start_idx + batchsize]

        else:

            excerpt = slice(start_idx, start_idx + batchsize)

        yield inputs[excerpt], targets[excerpt]







os.chdir("C:\Users\dell\Documents\PetImages")

lst = os.listdir()

train_cat = 

train_dog = 

train_x = 

train_y = 

count = 0

for anim in lst:

    os.chdir(anim)

    for img in os.listdir():

        image = Image.open(img).convert('L')

        width, height = image.size

        scale_factor = max(width, height)/100

        image =image.resize((int(width/scale_factor), int(height/scale_factor)))

        imgarr = np.asarray(image)

        zero_array = np.zeros((100,100))

        zero_array[:imgarr.shape[0], :imgarr.shape[1]] = imgarr

        imgarr = zero_array



        #imgarr = tf.keras.utils.normalize(imgarr, axis = 1, order=2)

        imgarr = (imgarr)/255.0

        if anim == "Cat":

                    imgarr = imgarr.flatten()

                    train_cat.append(imgarr)

        else :

                    imgarr = imgarr.flatten()

                    train_dog.append(imgarr)

        #imgplot = plt.imshow(imgarr)

        #print(imgarr)

        #plt.show()

        count+=1

        if count == 500:

            count = 0

            print()

            break

        print(str(count)+" Files Read.", sep=' ', end='r', flush=True)

    os.chdir('../')

print(len(train_cat))



label_cat = [[1,0] for i in train_cat]

label_dog = [[0,1] for i in train_dog]



train_x.extend(train_cat)

train_x.extend(train_dog)

train_y.extend(label_cat)

train_y.extend(label_dog)

print(len(train_x))

width = 100

height = 100

flat = width * height

class_output = 2

x = tf.placeholder(tf.float32, shape=[None, flat])

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



x_image = tf.reshape(x, [-1,100,100,1])

print(x_image)



W_conv1 = tf.Variable(tf.truncated_normal([5,5,1,32],stddev=0.1))

b_conv1 = tf.Variable(tf.constant(0.1, shape=[32]))



convolve1 = tf.nn.conv2d(x_image, W_conv1, strides=[1,1,1,1], padding="SAME") + b_conv1

h_conv1 = tf.nn.relu(convolve1)

conv1 = tf.nn.max_pool(h_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")

print(conv1)



W_conv2 = tf.Variable(tf.truncated_normal([5,5,32,64],stddev=0.1))

b_conv2 = tf.Variable(tf.constant(0.1, shape=[64]))



convolve2 = tf.nn.conv2d(conv1, W_conv2, strides=[1,1,1,1], padding="SAME") + b_conv2

h_conv2 = tf.nn.relu(convolve2)

conv2 = tf.nn.max_pool(h_conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")

print(conv2)



layer2_matrix = tf.reshape(conv2, [-1, conv2.shape[1] * conv2.shape[2] * 64])

W_fc1 = tf.Variable(tf.truncated_normal([int(layer2_matrix.shape[1]), 1024], stddev=0.1))

b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]))



fc1 = tf.matmul(layer2_matrix, W_fc1)+ b_fc1

h_fc1 = tf.nn.relu(fc1)

print(h_fc1)





keep_prob = tf.placeholder(tf.float32)

layer_drop = tf.nn.dropout(h_fc1, keep_prob)

print(layer_drop)





W_fc2 = tf.Variable(tf.truncated_normal([1024, class_output], stddev=0.1))

b_fc2 = tf.Variable(tf.constant(0.1, shape=[class_output]))



fc2 = tf.matmul(layer_drop, W_fc2)+ b_fc2

h_fc2 = tf.nn.relu(fc2)

print(h_fc2)





y_CNN = tf.nn.softmax(h_fc2)

print(y_CNN)





print(train_y)



print(y_*tf.log(y_CNN))



cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_CNN)))



train_step = tf.train.AdamOptimizer(1e-5).minimize(cross_entropy)



correct_prediction = tf.equal(tf.argmax(y_CNN, 1), tf.argmax(y_, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))



d = list(zip(train_x,train_y))

random.shuffle(d)

train_x, train_y = zip(*d)



print(train_y)

sess = tf.InteractiveSession()

sess.run(tf.global_variables_initializer())



n_epochs = 20





for n in range(2,1000):

    batch = (train_x[n-2:n], train_y[n-2:n])

    train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})

    loss = cross_entropy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})

    print("step %d, training accuracy %g, loss %g"%(n, float(train_accuracy), float(loss)))

    train_step.run(feed_dict={x:batch[0], y_:batch[1], keep_prob: 0.5})

The problem is that when I run the program, The loss first increases and then decreases to get to a constant value. While the accuracy only fluctuates between three values, 0, 0.5 and 1. Below is the image for same.

Can anyone please help me with this?