Importiamo le librerie e il dataset MNIST

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In [1]:

#from __future__ import print_function
import tensorflow as tf
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
from random import randint
import numpy as np
import time
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data

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Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

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# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
return tf.nn.relu(x)

def maxpool2d(x, k=2):
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],

# Create model
def conv_net(x, weights, biases, dropout):
# Reshape input picture
x = tf.reshape(x, shape=[-1, 28, 28, 1])

# Convolution Layer
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling)
conv1 = maxpool2d(conv1, k=2)

# Convolution Layer
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling)
conv2 = maxpool2d(conv2, k=2)

# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.nn.relu(fc1)
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)

# Output, class prediction
return out

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In [3]:

tf.reset_default_graph()
# Parameters
# START PARAMETRI DA MODIFICARE
learning_rate = 0.1
training_iters = 1000
# END PARAMETRI DA MODIFICARE
batch_size = 128
display_step = 10

# Network Parameters
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)
dropout = 0.75 # Dropout, probability to keep units

# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input], name="placeholder_x")
y = tf.placeholder(tf.float32, [None, n_classes], name="placeholder_y")
keep_prob = tf.placeholder(tf.float32, name="placeholder_dropout") #dropout (keep probability)
# Store layers weight & bias
weights = {
# 5x5 conv, 1 input, 32 outputs
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
# 5x5 conv, 32 inputs, 64 outputs
'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
# 1024 inputs, 10 outputs (class prediction)
'out': tf.Variable(tf.random_normal([1024, n_classes]))
}

biases = {
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_classes]))
}

# Construct model
pred = conv_net(x, weights, biases, keep_prob)

# Define loss and optimizer
argmax = tf.argmax(pred, 1)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))

# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.initialize_all_variables()

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WARNING:tensorflow:From <ipython-input-3-a74d8130799e>:51: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:

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In [4]:

start = time.time()
# Launch the graph
sess = tf.Session()
sess.run(init)
step = 1
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
# Run optimization op (backprop)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,
keep_prob: dropout})
if step % display_step == 0:
# Calculate batch loss and accuracy
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
y: batch_y,
keep_prob: 1.})
print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc)
step += 1
print "Optimization Finished!"

# Calculate accuracy for 128 mnist test images
print "Testing Accuracy:", \
sess.run(accuracy, feed_dict={x: mnist.test.images[:512],
y: mnist.test.labels[:512],
keep_prob: 1.})
print "Time elapsed: "+ str( time.time()-start)

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Optimization Finished!
Testing Accuracy: 0.154297
Time elapsed: 5.73013901711

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In [5]:

for i in range(0,10):

img = mnist.test.images[i]
classification = sess.run(argmax, feed_dict={x: [img],keep_prob: 1.})

plt.imshow(img.reshape(28, 28), cmap=plt.cm.binary)
plt.show()
print 'Number predicted is : ', classification

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Number predicted is :  [7]

Number predicted is :  [2]

Number predicted is :  [7]

Number predicted is :  [4]

Number predicted is :  [7]

Number predicted is :  [7]

Number predicted is :  [7]

Number predicted is :  [3]

Number predicted is :  [7]

Number predicted is :  [7]

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