Simple Multilayer Perceptron model does not converge in TensorFlow - deep-learning

Question

I am new to TensorFlow. Today I tried to implement my first model in TF but it returned strange results. I know that I am missing something here but I was not able to figure it out. Here is the story.
Model
I have a simple Multilayer Perceptron model with only a single hidden layer applied on MNIST databse. Layers are defined like [input(784) , hidden_layer(470) , output_layer(10)] with tanh as non-linearity for hidden layer and softmax as the loss for output layer. The optimizer I am using is Gradient Descent Algorithm with learning rate of 0.01. My mini batch size is 1 (I am training model with samples one by one).
My implementations :
First I implemented my model in C++ and got around 96% accuracy.Here is the repository : https://github.com/amin2ros/Artificog
I implemented the exact model in TensorFlow but surprisingly the model didn't converge at all. Here is the code.
Code:
import sys
import input_data
import matplotlib.pyplot as plt
from pylab import *
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
import tensorflow as tf
# Parameters
learning_rate = 0.1
training_epochs = 1
batch_size = 1
display_step = 1
# Network Parameters
n_hidden_1 = 470 # 1st layer num features
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)
# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
# Create model
def multilayer_perceptron(_X, _weights, _biases):
layer_1 = tf.tanh(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1']))
return tf.matmul(layer_1, _weights['out']) + _biases['out']
# Store layers weight & bias
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# Construct model
pred = multilayer_perceptron(x, weights, biases)
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax(pred)) # Softmax loss
optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(cost) #
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
m= 0
total_batch = int(mnist.train.num_examples/batch_size)
counter=0
#print 'count = ' , total_batch
#sys.stdin.read(1)
# Loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
label = tf.argmax(batch_ys,1).eval()[0]
counter+=1
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
wrong_prediction = tf.not_equal(tf.argmax(pred, 1), tf.argmax(y, 1))
missed=tf.cast(wrong_prediction, "float")
m += missed.eval({x: batch_xs, y: batch_ys})[0]
print "Sample #", counter , " - Label : " , label , " - Prediction :" , tf.argmax(pred, 1).eval({x: batch_xs, y: batch_ys})[0] ,\
"- Missed = " , m , " - Error Rate = " , 100 * float(m)/counter
print "Optimization Finished!"
I am very curious why this happens. Any help is appreciated.
Edit:
As commented below definition of cost function was incorrect so it should be like
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred,y))
Now model converges :)