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Test_theano.py
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Test_theano.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Apr 7 15:54:40 2016
@author: shengx
"""
#%% Load Data
import numpy as np
import theano
from theano import tensor as T
from Font import *
from utility import *
from NeuralNets import *
basis_size = 36
font_dir = 'Fonts'
input_letter = ['B','A','S','Q']
output_letter = ['R']
Fonts = Font(basis_size, font_dir, input_letter, output_letter )
#%%
trainInput, trainOutput, testInput, testOutput = Fonts.getLetterSets(10510,51)
trainInput = 1 - trainInput
trainOutput = 1 - trainOutput
testInput = 1 - testInput
testOutput = 1 - testOutput
n_train = trainInput.shape[0]
n_test = testInput.shape[0]
input_size = len(input_letter) * basis_size * basis_size
output_size = len(output_letter) * basis_size * basis_size
image_size = basis_size * basis_size
trainInput = trainInput.reshape((n_train,image_size*len(input_letter)))
trainOutput = trainOutput.reshape((n_train,image_size*len(output_letter)))
testInput = testInput.reshape((n_test,image_size*len(input_letter)))
testOutput = testOutput.reshape((n_test,image_size*len(output_letter)))
trainInput, trainOutput = shared_dataset(trainInput, trainOutput)
batch_size = 50
#%% building neural networks
rng1 = np.random.RandomState(1234)
rng2 = np.random.RandomState(2345)
rng3 = np.random.RandomState(1567)
rng4 = np.random.RandomState(1124)
nkerns = [2, 2]
learning_rate = 1
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x')
y = T.imatrix('y')
print('...building the model')
layer00_input = x[:,0:image_size].reshape((batch_size, 1, basis_size, basis_size))
layer01_input = x[:,image_size:2 * image_size].reshape((batch_size, 1, basis_size, basis_size))
layer02_input = x[:,2 * image_size:3 * image_size].reshape((batch_size, 1, basis_size, basis_size))
layer03_input = x[:,3 * image_size:4 * image_size].reshape((batch_size, 1, basis_size, basis_size))
# first convolutional layer
# image original size 50X50, filter size 5X5, filter number nkerns[0]
# after filtering, image size reduced to (36 - 3 + 1) = 34
# after max pooling, image size reduced to 34 / 2 = 17
layer00 = LeNetConvPoolLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer00_input,
image_shape=(batch_size, 1, basis_size, basis_size), # input image shape
filter_shape=(nkerns[0], 1, 3, 3),
poolsize=(2, 2)
)
layer01 = LeNetConvPoolLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer01_input,
image_shape=(batch_size, 1, basis_size, basis_size), # input image shape
filter_shape=(nkerns[0], 1, 3, 3),
poolsize=(2, 2)
)
layer02 = LeNetConvPoolLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer02_input,
image_shape=(batch_size, 1, basis_size, basis_size), # input image shape
filter_shape=(nkerns[0], 1, 3, 3),
poolsize=(2, 2)
)
layer03 = LeNetConvPoolLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer03_input,
image_shape=(batch_size, 1, basis_size, basis_size), # input image shape
filter_shape=(nkerns[0], 1, 3, 3),
poolsize=(2, 2)
)
# second convolutional layer
# input image size 23X23, filter size 4X4, filter number nkerns[1]
# after filtering, image size (17 - 4 + 1) = 14
# after max pooling, image size reduced to 14 / 2 = 7
layer10 = LeNetConvPoolLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer00.output,
image_shape=(batch_size, nkerns[0], 17, 17),
filter_shape=(nkerns[1], nkerns[0], 4, 4),
poolsize=(2, 2)
)
layer11 = LeNetConvPoolLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer01.output,
image_shape=(batch_size, nkerns[0], 17, 17),
filter_shape=(nkerns[1], nkerns[0], 4, 4),
poolsize=(2, 2)
)
layer12 = LeNetConvPoolLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer02.output,
image_shape=(batch_size, nkerns[0], 17, 17),
filter_shape=(nkerns[1], nkerns[0], 4, 4),
poolsize=(2, 2)
)
layer13 = LeNetConvPoolLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer03.output,
image_shape=(batch_size, nkerns[0], 17, 17),
filter_shape=(nkerns[1], nkerns[0], 4, 4),
poolsize=(2, 2)
)
# layer 2 input size = 2 * 4 * 7 * 7 = 392
layer2_input = T.concatenate([layer10.output.flatten(2), layer11.output.flatten(2), layer12.output.flatten(2), layer13.output.flatten(2)],
axis = 1)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer2_input,
n_in=nkerns[1] * len(input_letter) * 7 * 7,
n_out=50,
activation=T.nnet.sigmoid
)
layer3 = HiddenLayer(
np.random.RandomState(np.random.randint(10000)),
input=layer2.output,
n_in=50,
n_out=50,
activation=T.nnet.sigmoid
)
layer4 = BinaryLogisticRegression(
np.random.RandomState(np.random.randint(10000)),
input=layer3.output,
n_in=50,
n_out=basis_size * basis_size,
)
cost = layer4.negative_log_likelihood(y)
error = ((y - layer4.y_pred)**2).sum()
params = (layer4.params
+ layer3.params
+ layer2.params
+ layer10.params + layer11.params + layer12.params + layer13.params
+ layer00.params + layer01.params + layer02.params + layer03.params)
grads = T.grad(cost, params)
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)
]
#
#test_model = theano.function(
# inputs = [index],
# outputs = cost,
# givens={
# x: testInput[index * batch_size: (index + 1) * batch_size],
# y: testOutput[index * batch_size: (index + 1) * batch_size]
# }
# )
train_model = theano.function(
inputs = [index],
outputs = cost,
updates=updates,
givens={
x: trainInput[index * batch_size: (index + 1) * batch_size],
y: trainOutput[index * batch_size: (index + 1) * batch_size]
}
)
#%% training the model
n_train_batches = 210
n_epochs = 1500
epoch = 0
while (epoch < n_epochs):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
print((' epoch %i, minibatch %i/%i.') % (epoch, minibatch_index +1, n_train_batches))
#test_losses = [test_model(i) for i in range(n_test_batches)]
#test_score = np.mean(test_losses)
#%% predict output
predict_model = theano.function(
inputs = [x],
outputs = layer4.p_y_given_x,
on_unused_input='ignore'
)
predicted_values = predict_model(testInput[0:50])
#%% compare output
n = 3
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
output_img = predicted_values
output_img = output_img.reshape(50,36,36)
output_img = np.asarray(output_img, dtype = 'float64') /256
plt.figure(1)
plt.subplot(121)
plt.imshow(output_img[n,:,:],interpolation="nearest",cmap='Greys')
plt.subplot(122)
plt.imshow(testOutput[n,:].reshape((basis_size,basis_size)),interpolation="nearest",cmap='Greys')