def evaluate_model(learning_rate=0.001,
n_epochs=100,
nkerns=[16, 40, 50, 60],
batch_size=20):
"""
Network for classification of MNIST database
:type learning_rate: float
:param learning_rate: this is the initial learning rate used
(factor for the stochastic gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
:type batch_size: int
:param batch_size: the batch size for training
"""
print("Evaluating model")
rng = numpy.random.RandomState(23455)
# loading the data
datasets = load_test_data()
valid_set_x, valid_set_y = datasets[0]
test_set_x, test_set_y = datasets[1]
# compute number of minibatches for training, validation and testing
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_valid_batches //= batch_size
n_test_batches //= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# start-snippet-1
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
loaded_params = numpy.load('../saved_models/model.npy')
layer4_W, layer4_b, layer3_W, layer3_b, layer2_W, layer2_b, layer1_W, layer1_b, layer0_W, layer0_b = loaded_params
######################
# BUILD ACTUAL MODEL #
######################
print('Building the model...')
chosen_height = 64
chosen_width = 64
# Reshape matrix of rasterized images of shape (batch_size, 32 * 32)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (32, 32) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 3, chosen_height, chosen_width))
# Construct the first convolutional pooling layer:
# filtering does not reduce the layer size because we use padding
# maxpooling reduces the size to (32/2, 32/2) = (16, 16)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 16, 16)
layer0 = MyConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 3, chosen_height,
chosen_width),
p1=2,
p2=2,
filter_shape=(nkerns[0], 3, 5, 5),
poolsize=(2, 2))
# Construct the second convolutional pooling layer:
# filtering does not reduce the layer size because we use padding
# maxpooling reduces the size to (16/2, 16/2) = (8, 8)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 5, 5)
layer1 = MyConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0],
chosen_height / 2, chosen_width / 2),
p1=2,
p2=2,
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2))
# Construct the third convolutional pooling layer
# filtering does not reduce the layer size because we use padding
# maxpooling reduces the size to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[2], 4, 4)
layer2 = MyConvPoolLayer(rng,
input=layer1.output,
image_shape=(batch_size, nkerns[1],
chosen_height / 4, chosen_width / 4),
p1=2,
p2=2,
filter_shape=(nkerns[2], nkerns[1], 5, 5),
poolsize=(2, 2))
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[2] * 4 * 4),
# or (500, 20 * 4 * 4) = (500, 320) with the default values.
layer3_input = layer2.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer3 = HiddenLayer(rng,
input=layer3_input,
n_in=nkerns[2] * (chosen_height / 8) *
(chosen_width / 8),
n_out=800,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer4 = LogisticRegression(input=layer3.output, n_in=800, n_out=6)
cost = layer4.negative_log_likelihood(y)
predicted_output = layer4.y_pred
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
layer4.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
validate_model = theano.function(
[index],
layer4.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
# create a list of all model parameters to be fit by gradient descent
params = layer4.params + layer3.params + layer2.params + layer1.params + layer0.params
#Loading the model
# f = file('../saved_models/model317.save.npy', 'r')
# params = cPickle.load(f)
# print(params)
# f.close()
# # layer4.params, layer3.params, layer2.params, layer1.params, layer0.params = params
# # layer4.W, layer4.b = layer4.params
# # layer3.W, layer3.b = layer3.params
# # layer2.W, layer2.b = layer2.params
# # layer1.W, layer1.b = layer1.params
# # layer0.W, layer0.b = layer0.params
# layer4.W, layer4.b, layer3.W, layer3.b, layer2.W, layer2.b, layer1.W, layer1.b, layer0.W, layer0.b = params
# layer4.params = [layer4.W, layer4.b]
# layer3.params = [layer3.W, layer3.b]
# layer2.params = [layer2.W, layer2.b]
# layer1.params = [layer1.W, layer1.b]
# layer0.params = [layer0.W, layer0.b]
# x = cPickle.load(f)
# layer4.params = [layer4.W, layer4.b]
# layer3.params = [layer3.W, layer3.b]
# layer2.params = [layer2.W, layer2.b]
# layer1.params = [layer1.W, layer1.b]
# layer0.params = [layer0.W, layer0.b]
# test it on the test set
test_losses = [test_model(i) for i in range(n_test_batches)]
validation_losses = [validate_model(i) for i in range(n_valid_batches)]
test_score = numpy.mean(test_losses)
validation_score = numpy.mean(validation_losses)
print((' Validation error is %f %%') % (validation_score * 100.))
print((' Test error is %f %%') % (test_score * 100.))
def evaluate_model(learning_rate=0.001,
n_epochs=100,
nkerns=[16, 40, 50, 60],
batch_size=20):
"""
Network for classification of MNIST database
:type learning_rate: float
:param learning_rate: this is the initial learning rate used
(factor for the stochastic gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
:type batch_size: int
:param batch_size: the batch size for training
"""
print("Evaluating model")
rng = numpy.random.RandomState(23455)
# loading the data1
datasets = load_test_data(1)
valid_set_x, valid_set_y = datasets[0]
test_set_x, test_set_y = datasets[1]
# compute number of minibatches for training, validation and testing
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_valid_batches //= batch_size
n_test_batches //= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# start-snippet-1
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
loaded_params = numpy.load('../saved_models/model1.npy')
layer4_W, layer4_b, layer3_W, layer3_b, layer2_W, layer2_b, layer1_W, layer1_b, layer0_W, layer0_b = loaded_params
######################
# BUILD ACTUAL MODEL #
######################
print('Building the model...')
# Reshape matrix of rasterized images of shape (batch_size, 32 * 32)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (32, 32) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 64, 88))
# Construct the first convolutional pooling layer:
# filtering does not reduce the layer size because we use padding
# maxpooling reduces the size to (32/2, 32/2) = (16, 16)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 16, 16)
layer0 = MyConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 64, 88),
p1=2,
p2=2,
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
W=layer0_W,
b=layer0_b)
# Construct the second convolutional pooling layer:
# filtering does not reduce the layer size because we use padding
# maxpooling reduces the size to (16/2, 16/2) = (8, 8)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 5, 5)
layer1 = MyConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 32, 44),
p1=2,
p2=2,
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2),
W=layer1_W,
b=layer1_b)
# Construct the third convolutional pooling layer
# filtering does not reduce the layer size because we use padding
# maxpooling reduces the size to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[2], 4, 4)
layer2 = MyConvPoolLayer(rng,
input=layer1.output,
image_shape=(batch_size, nkerns[1], 16, 22),
p1=2,
p2=2,
filter_shape=(nkerns[2], nkerns[1], 5, 5),
poolsize=(2, 2),
W=layer2_W,
b=layer2_b)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[2] * 4 * 4),
# or (500, 20 * 4 * 4) = (500, 320) with the default values.
layer3_input = layer2.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer3 = HiddenLayer(rng,
input=layer3_input,
n_in=nkerns[2] * 8 * 11,
n_out=800,
activation=T.tanh,
W=layer3_W,
b=layer3_b)
# classify the values of the fully-connected sigmoidal layer
layer4 = LogisticRegression(input=layer3.output,
n_in=800,
n_out=6,
W=layer4_W,
b=layer4_b)
cost = layer4.negative_log_likelihood(y)
predicted_output = layer4.y_pred
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
layer4.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
val_model_preds = theano.function(
[index],
layer4.prediction(),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
})
validate_model = theano.function(
[index],
layer4.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
# create a list of all model parameters to be fit by gradient descent
params = layer4.params + layer3.params + layer2.params + layer1.params + layer0.params
val_preds = [val_model_preds(i) for i in range(n_valid_batches)]
#print(val_preds)
#preds = numpy(val_preds)
preds = []
for pred in val_preds:
for p in pred:
preds.append(p)
#preds = val_preds.reshape(valid_set_x.get_value(borrow=True).shape[0])
actual_labels = load_test_data(1, 2)
n = len(actual_labels)
confusion_matrix = numpy.zeros((6, 6))
for i in range(n):
confusion_matrix[int(actual_labels[i])][preds[i]] += 1
print(confusion_matrix)
correct = 0.0
for i in range(n):
if (preds[i] == int(actual_labels[i])):
correct += 1.0
accuracy = correct / n
print("Number of correctly classified : ", correct)
print("Test accuracy is", accuracy * 100)
from __future__ import print_function
import timeit
import gzip
import copy
import numpy
import math
import theano
import theano.tensor as T
from logistic_regression import LogisticRegression
from hidden_layer import HiddenLayer
from loading_data import load_data, load_test_data
from conv_layers import LeNetConvPoolLayer, MyConvPoolLayer, MyConvLayer
import cPickle
import os
actual_labels = load_test_data(1, 1)
n = len(actual_labels)
preds1 = list(numpy.load("../predicted_labels1.npy").reshape(n))
preds2 = list(numpy.load("../predicted_labels2.npy").reshape(n))
preds3 = list(numpy.load("../predicted_labels3.npy").reshape(n))
preds = []
for i in range(len(preds1)):
# if (preds1[i] == preds2[i]):
# preds.append(preds1[i])
# elif (preds1[i] == preds3[i]):
# preds.append(preds1[i])
# elif (preds2[i] == preds3[i]):
