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demo_conv2_L3.py
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demo_conv2_L3.py
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#!/usr/bin/env python
import tensorflow as tf
import numpy as np
import input_data
from PIL import Image
from matplotlib import pyplot
from tensorflow.python.ops import control_flow_ops
#=========
def myplot(numFeatures,X_val, L3a_val, L3_val, m, i):
#====================================
print 'myplot to save the images'
# plot original image and first and second components of output
# >9 plot, fig.add_subplot(4,4,11) , index start with 1
fig = pyplot.figure()
ax = fig.add_subplot(3,8,1)
ax.axis('off');
pyplot.imshow(X_val[m,:,:,0]) #, cmap='gray')
# recall that the convOp output (filtered image) is actually a "minibatch",
# of size 1 here, so we take index 0 in the first dimension:
step= numFeatures/8
for k in range(0, numFeatures, step):
ax=fig.add_subplot(3,8,k/step +9); ax.axis('off'); pyplot.imshow(L3a_val[m,:,:,k])#, cmap='gray')
ax=fig.add_subplot(3,8,k/step +17); ax.axis('off'); pyplot.imshow(L3_val[m,:,:,k])#, cmap='gray')
pyplot.savefig('t{0}L3_{1}.jpg'.format(m,i) )
# fig.close()
#====================================
# input numpy ndarray
def calc_shape(l1a):
shape0=l1a.shape
#print 'l1a.shape = {0}'.format(shape0)
shape1=shape0[1:3] # (?,28,28,16) --> (28,28)
#print 'l1a.shape trucated to {0}'.format(shape1)
shape1=np.append([2], shape1)# --> [2,28,28]
#print 'l1a.shape trucated and reshaped to {0}'.format(shape1)
return shape1
#=====================================
def Ln_sampling(l1a):
l1a_0=l1a[0,:,:,0] # the 0-th output feature for the 0th input, [28,28]
#print 'l1a sample0 shape = {0}'.format(l1a_0.shape)
l1a_15=l1a[0,:,:,15] # the 15-th output feature for the 0th input
#l1a_sample=tf.concat(0, [l1a_0, l1a_15]) # concate at dim 0
return np.append([l1a_0], [l1a_15], 0)
#==============================
batch_size = 128
test_size = 256
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def model(X, w, w2, w3, w4, w_o):
l1a = tf.nn.relu(tf.nn.conv2d(X, w, # l1a shape=(?, 28, 28, 16)
strides=[1, 1, 1, 1], padding='SAME'))
l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1], # l1 shape=(?, 14, 14, 16)
strides=[1, 2, 2, 1], padding='SAME')
#l1 = tf.nn.max_pool(l1a, ksize=[1, 4, 4, 1], # l1 shape=(?, 7, 7, 16)
# strides=[1, 4, 4, 1], padding='SAME')
l2a = tf.nn.relu(tf.nn.conv2d(l1, w2, # l2a shape=(?, 14, 14, 32)
strides=[1, 1, 1, 1], padding='SAME'))
l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1], # l2 shape=(?, 7, 7, 32)
strides=[1, 2, 2, 1], padding='SAME')
l3a = tf.nn.relu(tf.nn.conv2d(l2, w3, # l3a shape=(?, 7, 7, 64)
strides=[1, 1, 1, 1], padding='SAME'))
l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1], # l3 shape=(?, 4, 4, 64)
strides=[1, 2, 2, 1], padding='SAME')
#l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]]) # reshape to (?, 2048)
l3_reshape = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]]) # reshape to (?, 4*4*32)
l4 = tf.nn.relu(tf.matmul(l3_reshape, w4))
pyx = tf.matmul(l4, w_o)
return pyx, l3a, l3
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
trX = trX.reshape(-1, 28, 28, 1) # 28x28x1 input img
teX = teX.reshape(-1, 28, 28, 1) # 28x28x1 input img
X = tf.placeholder("float", [None, 28, 28, 1])
Y = tf.placeholder("float", [None, 10])
w = init_weights([3, 3, 1, 16]) # 3x3x1 conv, 16 outputs
w2 = init_weights([3, 3, 16, 32]) # 3x3x16 conv, 32 outputs
w3 = init_weights([3, 3, 32, 64]) # 3x3x32 conv, 64 outputs
# w4 = init_weights([128 * 4 * 4, 625]) # FC 128 * 4 * 4 inputs, 625 outputs
w4 = init_weights([64 * 4 * 4, 128]) # FC 64 * 4 * 4 inputs, 128 outputs
#w_o = init_weights([625, 10]) # FC 625 inputs, 10 outputs (labels)
w_o = init_weights([128, 10]) # FC 128 inputs, 10 outputs (labels)
py_x, L3a, L3 = model(X, w, w2, w3, w4, w_o)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(py_x, 1)
#randomly choose 256 images from test data
test_indices = np.arange(len(teX)) # Get A Test Batch
np.random.shuffle(test_indices)
test_indices = test_indices[0:test_size]
# Launch the graph in a session
with tf.Session() as sess:
# you need to initialize all variables
tf.initialize_all_variables().run()
for i in range(10):
training_batch = zip(range(0, len(trX), batch_size),
range(batch_size, len(trX), batch_size))
for start, end in training_batch:
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end] })
result= sess.run([predict_op, L3a, L3], feed_dict={X: teX[test_indices], Y: teY[test_indices]})
argMax=result[0]
"""
print '=== argMax ============='
print type(argMax)
print len(argMax)
"""
X_val=teX[test_indices]
Y_val=teY[test_indices]
print 'teY[0]= {0}'.format(Y_val[0])
L1a_val=result[1]
L1_val=result[2]
# will L1a_val reflect the change of weightings ?
print(i, np.mean(np.argmax(Y_val, axis=1) == argMax) )
print type(X_val)
print X_val.shape
print type(L1_val)
print L1_val.shape
print '============================='
myplot(64, X_val, L1a_val, L1_val, 0, i)
myplot(64, X_val, L1a_val, L1_val, 31, i)