def evaluate_lenet5(learning_rate=0.01, n_epochs=2000,
dataset='file_622.pkl.gz',
display_filters = True,
nkerns=[32, 48, 64, 128, 256], batch_size=500): #batch_size = 500
""" Demonstrates lenet on MNIST dataset
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: path to the dataset used for training /testing (MNIST here)
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
"""
rng = numpy.random.RandomState(23455)
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
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_train_batches //= batch_size
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
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 100, 46)) #28,28
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (100-3+1 , 46-3+1) = (98, 44)
# maxpooling reduces this further to (98/2, 44/2) = (49, 22)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 49, 22)
layer0 = LeNetConvPoolLayer1(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 100, 46),
filter_shape=(nkerns[0], 1, 3, 3),
poolsize=(2, 2)
)
layer0_1 = LeNetConvPoolLayer( #Conv ReLU
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 49, 22),
filter_shape=(nkerns[1], nkerns[0], 3, 3),
poolsize=(2, 2)
)
layer0_2 = LeNetConvPoolLayer( #Conv ReLU
rng,
input=layer0_1.output,
image_shape=(batch_size, nkerns[1], 47, 20),
filter_shape=(nkerns[1], nkerns[1], 3, 3),
poolsize=(2, 2)
)
layer0_3 = LeNetConvPoolLayer( #Conv ReLU
rng,
input=layer0_2.output,
image_shape=(batch_size, nkerns[1], 45, 18),
filter_shape=(nkerns[2], nkerns[1], 3, 3),
poolsize=(2, 2)
)
layer1 = LeNetConvPoolLayer( #Conv ReLU
rng,
input=layer0_3.output,
image_shape=(batch_size, nkerns[2], 43, 16),
filter_shape=(nkerns[2], nkerns[2], 3, 3),
poolsize=(2, 2)
)
layer1_1 = LeNetConvPoolLayer( #Conv ReLU
rng,
input=layer1.output,
image_shape=(batch_size, nkerns[2], 41, 14),
filter_shape=(nkerns[3], nkerns[2], 3, 3),
poolsize=(2, 2)
)
layer1_2 = LeNetConvPoolLayer( #Conv ReLU
rng,
input=layer1_1.output,
image_shape=(batch_size, nkerns[3], 39, 12),
filter_shape=(nkerns[3], nkerns[3], 3, 3),
poolsize=(2, 2)
)
layer1_3 = LeNetConvPoolLayer( #Conv ReLU
rng,
input=layer1_2.output,
image_shape=(batch_size, nkerns[3], 37, 10),
filter_shape=(nkerns[3], nkerns[3], 3, 3),
poolsize=(2, 2)
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (35-5+1, 8-5+1) = (31, 4)
# maxpooling reduces this further to (31/2, 4/2) = (15, 2)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 15, 2)
layer2_1 = LeNetConvPoolLayer1(
rng,
input=layer1_3.output,
image_shape=(batch_size, nkerns[3], 35, 8),
filter_shape=(nkerns[4], nkerns[3], 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[1] * 22 * 8),
# or (500, 50 * 22 * 8) = (500, 8800) with the default values.
layer2_input = layer2_1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[4] * 15 * 2,
n_out=800,
activation=T.tanh
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=800, n_out=5)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
layer3.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],
layer3.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 = layer3.params + layer2_1.params + layer1_3.params + layer1_2.params + layer1_1.params + layer1.params+ layer0_3.params + layer0_2.params + layer0_1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)
]
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# end-snippet-1
###############
# TRAIN MODEL #
###############
print('... training')
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience // 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in range(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print('training @ iter = ', iter)
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i
in range(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [
test_model(i)
for i in range(n_test_batches)
]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
"""if patience <= iter:
done_looping = True
break"""
end_time = timeit.default_timer()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print(('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.)), file=sys.stderr)
def evaluate_lenet5(initial_learning_rate=0.1,
learning_rate_decay=1,
dropout_rates=[0.2, 0.2, 0.2, 0.5],
n_epochs=200,
dataset='mnist.pkl.gz',
nkerns=[20, 50],
batch_size=500):
""" Demonstrates lenet on MNIST dataset
:type learning_rate: float
:param learning_rate: learning rate used (factor for the stochastic
gradient)
:type learning_rate_decay: float
:param learning_rate_decay: learning rate decay used (1 means learning rate decay is deactivated)
:type dropout_rates: list of float
:param dropout_rates: dropout rate used for each layer (input layer, 1st filtered layer, 2nd filtered layer, fully connected layer)
:type n_epochs: int
:param n_epochs: maximal number of epochs to run the optimizer
:type dataset: string
:param dataset: path to the dataset used for training /testing (MNIST here)
:type nkerns: list of ints
:param nkerns: number of kernels on each layer
"""
rng = numpy.random.RandomState(23455)
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
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_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
epoch = T.scalar()
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
learning_rate = theano.shared(
numpy.asarray(initial_learning_rate, dtype=theano.config.floatX))
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 28, 28))
layer0_input_dropout = _dropout_from_layer(rng, layer0_input,
dropout_rates[0])
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0_dropout = DropoutLeNetConvPoolLayer(rng,
input=layer0_input_dropout,
image_shape=(batch_size, 1, 28,
28),
filter_shape=(nkerns[0], 1, 5,
5),
poolsize=(2, 2),
dropout_rate=dropout_rates[1])
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2),
W=layer0_dropout.W * (1 - dropout_rates[0]),
b=layer0_dropout.b)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer1_dropout = DropoutLeNetConvPoolLayer(rng,
input=layer0_dropout.output,
image_shape=(batch_size,
nkerns[0], 12, 12),
filter_shape=(nkerns[1],
nkerns[0], 5, 5),
poolsize=(2, 2),
dropout_rate=dropout_rates[2])
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2),
W=layer1_dropout.W * (1 - dropout_rates[1]),
b=layer1_dropout.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[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layer2_dropout_input = layer1_dropout.output.flatten(2)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2_dropout = DropoutHiddenLayer(rng,
input=layer2_dropout_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh,
dropout_rate=dropout_rates[3])
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh,
W=layer2_dropout.W * (1 - dropout_rates[2]),
b=layer2_dropout.b)
# classify the values of the fully-connected sigmoidal layer
layer3_dropout = LogisticRegression(input=layer2_dropout.output,
n_in=500,
n_out=10)
layer3 = LogisticRegression(input=layer2.output,
n_in=500,
n_out=10,
W=layer3_dropout.W * (1 - dropout_rates[-1]),
b=layer3_dropout.b)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
dropout_cost = layer3_dropout.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
layer3.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],
layer3.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 = layer3_dropout.params + layer2_dropout.params + layer1_dropout.params + layer0_dropout.params
# create a list of gradients for all model parameters
grads = T.grad(dropout_cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
updates = [(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)]
train_model = theano.function(
[index],
dropout_cost,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
# Theano function to decay the learning rate
decay_learning_rate = theano.function(
inputs=[],
outputs=learning_rate,
updates={learning_rate: learning_rate * learning_rate_decay})
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [
validate_model(i) for i in xrange(n_valid_batches)
]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = [
test_model(i) for i in xrange(n_test_batches)
]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
new_learning_rate = decay_learning_rate()
end_time = timeit.default_timer()
print('Optimization complete.')
print(
'Best validation score of %f %% obtained at iteration %i, '
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
def __init__(self, rng, input, filter_shapes, image_shape, poolsize,
layer_sizes, dropout_rates, activations):
"""
Allocate a cnn_mlp (ConvNet followed by MLP) with shared variable internal parameters.
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dtensor4
:param input: symbolic image tensor, of shape image_shape
:type filter_shapes: list of (list of length 4)
:param filter_shapes: list of the filters whith their respective properties ((number of kernels, num input feature maps,
filter height, filter width), ...)
len(filter_shapes) = number of LeNetConvPoolLayer layers
:type image_shape: tuple or list of length 4
:param image_shape: (batch size, num input feature maps,
image height, image width)
:type poolsize: tuple or list of length 2
:param poolsize: the downsampling (pooling) factor (#rows, #cols)
:type layer_sizes: list of int
:param layer_sizes: sizes (number of units) of each HiddenLayer (
len(layer_sizes) = number of HiddenLayer layers)
:type dropout_rates: list of float
:param dropout_rates: dropout rate used for each layer (including dropout on the input)
:type activations: list of theano.function
:param activations: list of the activation functions to use at each layer
"""
#######################################
# Set up all the convolutional layers #
#######################################
self.layers = []
self.dropout_layers = []
next_layer_input = input.reshape(image_shape)
next_dropout_layer_input = _dropout_from_layer(rng,
next_layer_input,
p=dropout_rates[0])
layer_counter = 0
for i in range(len(filter_shapes)):
filter_shape = filter_shapes[i]
next_dropout_layer = DropoutLeNetConvPoolLayer(
rng=rng,
input=next_dropout_layer_input,
image_shape=image_shape,
filter_shape=filter_shape,
poolsize=poolsize,
dropout_rate=dropout_rates[layer_counter + 1],
activation=activations[layer_counter])
self.dropout_layers.append(next_dropout_layer)
next_dropout_layer_input = next_dropout_layer.output
# Reuse parameters from the dropout layer here
next_layer = LeNetConvPoolLayer(
rng=rng,
input=next_layer_input,
image_shape=image_shape,
filter_shape=filter_shape,
W=next_dropout_layer.W * (1 - dropout_rates[layer_counter]),
b=next_dropout_layer.b,
poolsize=poolsize,
activation=activations[layer_counter])
self.layers.append(next_layer)
next_layer_input = next_layer.output
image_shape = (image_shape[0], filter_shape[0],
(image_shape[2] - filter_shape[2] + 1) /
poolsize[0],
(image_shape[3] - filter_shape[3] + 1) /
poolsize[1])
layer_counter += 1
################################
# Set up all the hidden layers #
################################
weight_matrix_sizes = zip(layer_sizes, layer_sizes[1:])
next_layer_input = next_layer_input.flatten(2)
next_dropout_layer_input = next_dropout_layer_input.flatten(2)
assert (
layer_sizes[0] == numpy.prod(image_shape[1:])
), "The dimension of the first hidden layer does not match last convolutional layer size."
for n_in, n_out in weight_matrix_sizes[:-1]:
next_dropout_layer = DropoutHiddenLayer(
rng=rng,
input=next_dropout_layer_input,
activation=activations[layer_counter],
n_in=n_in,
n_out=n_out,
dropout_rate=dropout_rates[layer_counter + 1])
self.dropout_layers.append(next_dropout_layer)
next_dropout_layer_input = next_dropout_layer.output
# Reuse the paramters from the dropout layer here
next_layer = HiddenLayer(
rng=rng,
input=next_layer_input,
activation=activations[layer_counter],
# scale the weight matrix W with (1-p)
W=next_dropout_layer.W * (1 - dropout_rates[layer_counter]),
b=next_dropout_layer.b,
n_in=n_in,
n_out=n_out)
self.layers.append(next_layer)
next_layer_input = next_layer.output
layer_counter += 1
###########################
# Set up the output layer #
###########################
n_in, n_out = weight_matrix_sizes[-1]
dropout_output_layer = LogisticRegression(
input=next_dropout_layer_input, n_in=n_in, n_out=n_out)
self.dropout_layers.append(dropout_output_layer)
# Again, reuse paramters in the dropout output.
output_layer = LogisticRegression(
input=next_layer_input,
# scale the weight matrix W with (1-p)
W=dropout_output_layer.W * (1 - dropout_rates[-1]),
b=dropout_output_layer.b,
n_in=n_in,
n_out=n_out)
self.layers.append(output_layer)
# Use the negative log likelihood of the logistic regression layer as
# the objective.
self.dropout_negative_log_likelihood = self.dropout_layers[
-1].negative_log_likelihood
self.dropout_errors = self.dropout_layers[-1].errors
self.negative_log_likelihood = self.layers[-1].negative_log_likelihood
self.errors = self.layers[-1].errors
# Grab all the parameters together.
self.params = [
param for layer in self.dropout_layers for param in layer.params
]