def main():
#import pdb
#pdb.set_trace()
###################
#BUILD THE DATASET#
###################
print 'build the dataset'
import pdb
pdb.set_trace()
train_set = SVHN_On_Memory(which_set='train')
test_set = SVHN_On_Memory(which_set='test')
train_setX, valid_setX = split(train_set.X, [50000], axis=0)
train_sety, valid_sety = split(train_set.y, [50000], axis=0)
test_set.y = nonzero(test_set.y)[1]
train_sety = nonzero(train_sety)[1]
valid_sety = nonzero(valid_sety)[1]
#import pdb
#pdb.set_trace()
##################
#BUILD THE LAYERS#
##################
print 'build the layers'
input_size = len(train_setX[0])
h1 = NoisyRELU(prev_layer_size=input_size, this_layer_size=1000, threshold=5, noise_factor=1)
h2 = NoisyRELU(prev_layer_size=h1.get_size(), this_layer_size=1000, threshold=5, noise_factor=1)
h3 = NoisyRELU(prev_layer_size=h2.get_size(), this_layer_size=1000, threshold=5, noise_factor=1)
output_layer = Softmax(prev_layer_size=h1.this_layer_size, this_layer_size=10,
W_range=[0,0], b_range=[0,0])
#y_layer = Sigmoid(prev_layer_size=h2.this_layer_size, this_layer_size=[10,1])
mlp = MLP(input_size=input_size,
layers=[h1, h2, h3, output_layer],
train_set=[train_setX, train_sety],
valid_set=[valid_setX, valid_sety],
test_set=[test_set.X, test_set.y],
error_function=loglikehood,
batch_size=20,
learning_rate=0.1)
print 'start training'
mlp.train(save_path='1000-1000-1000-noisy.pkl', save_freq=1)
def main():
#import pdb
#pdb.set_trace()
###################
#BUILD THE DATASET#
###################
print 'build the dataset'
train_set = MNIST(which_set='train', one_hot=False)
test_set = MNIST(which_set='test', one_hot=False)
train_setX, valid_setX = split(train_set.X, [50000], axis=0)
train_sety, valid_sety = split(train_set.y, [50000], axis=0)
#import pdb
#pdb.set_trace()
##################
#BUILD THE LAYERS#
##################
print 'build the layers'
input_size = len(train_setX[0])
h1 = Tanh(prev_layer_size=input_size, this_layer_size=2000)
output_layer = Softmax(prev_layer_size=h1.this_layer_size, this_layer_size=10,
W_range=[0,0], b_range=[0,0])
#y_layer = Sigmoid(prev_layer_size=h2.this_layer_size, this_layer_size=[10,1])
mlp = MLP(input_size=input_size,
layers=[h1, output_layer],
train_set=[train_setX, train_sety],
valid_set=[valid_setX, valid_sety],
test_set=[test_set.X, test_set.y],
error_function=loglikehood,
batch_size=20,
learning_rate=0.1)
print 'start training'
mlp.train()
#p = plt.plot(mlp.epoch, mlp.valid_error)
#plots.append(p)
with open('batches_clean.pkl', 'wb') as bat:
cPickle.dump(mlp.epoch, bat)
with open('errors_clean.pkl', 'wb') as err:
cPickle.dump(mlp.valid_error, err)
with open('legends_clean.pkl', 'wb') as leg:
cPickle.dump(['2000-tanh'], leg)
def main():
###################
#BUILD THE DATASET#
###################
print 'build the dataset'
train_set = MNIST(which_set='train', one_hot=False)
test_set = MNIST(which_set='test', one_hot=False)
train_setX, valid_setX = split(train_set.X, [50000], axis=0)
train_sety, valid_sety = split(train_set.y, [50000], axis=0)
#import pdb
#pdb.set_trace()
##################
#BUILD THE LAYERS#
##################
print 'build the layers'
input_size = len(train_setX[0])
h1 = NoisyRELU(prev_layer_size=input_size,
this_layer_size=200,
noise_factor=0,
activation_threshold=0)
output_layer = Softmax(prev_layer_size=h1.this_layer_size,
this_layer_size=10,
W_range=[0, 0],
b_range=[0, 0])
#y_layer = Sigmoid(prev_layer_size=h2.this_layer_size, this_layer_size=[10,1])
# import pdb
# pdb.set_trace()
print 'build the model'
mlp = MLP(input_size=input_size,
layers=[h1, output_layer],
train_set=[train_setX, train_sety],
valid_set=[valid_setX, valid_sety],
test_set=[test_set.X, test_set.y],
error_function=loglikehood,
batch_size=20)
print 'start training'
mlp.train_batch(100000)
def main():
###################
#BUILD THE DATASET#
###################
print 'build the dataset'
train_set = MNIST(which_set='train', one_hot=False)
test_set = MNIST(which_set='test', one_hot=False)
train_setX, valid_setX = split(train_set.X, [50000], axis=0)
train_sety, valid_sety = split(train_set.y, [50000], axis=0)
#import pdb
#pdb.set_trace()
##################
#BUILD THE LAYERS#
##################
print 'build the layers'
input_size = len(train_setX[0])
h1 = NoisyRELU(prev_layer_size=input_size, this_layer_size=200, noise_factor=0, activation_threshold=0)
output_layer = Softmax(prev_layer_size=h1.this_layer_size, this_layer_size=10,
W_range=[0,0], b_range=[0,0])
#y_layer = Sigmoid(prev_layer_size=h2.this_layer_size, this_layer_size=[10,1])
# import pdb
# pdb.set_trace()
print 'build the model'
mlp = MLP(input_size=input_size,
layers=[h1, output_layer],
train_set=[train_setX, train_sety],
valid_set=[valid_setX, valid_sety],
test_set=[test_set.X, test_set.y],
error_function=loglikehood,
batch_size=20)
print 'start training'
mlp.train_batch(100000)
def main():
#import pdb
#pdb.set_trace()
###################
#BUILD THE DATASET#
###################
print 'build the dataset'
train_set = MNIST(which_set='train', one_hot=False)
test_set = MNIST(which_set='test', one_hot=False)
train_setX, valid_setX = split(train_set.X, [50000], axis=0)
train_sety, valid_sety = split(train_set.y, [50000], axis=0)
#import pdb
#pdb.set_trace()
##################
#BUILD THE LAYERS#
##################
print 'build the layers'
input_size = len(train_setX[0])
h1 = NoisyRELU(prev_layer_size=input_size, this_layer_size=2000, noise_factor=1, threshold=5)
output_layer = Softmax(prev_layer_size=h1.this_layer_size, this_layer_size=10,
W_range=[0,0], b_range=[0,0])
#y_layer = Sigmoid(prev_layer_size=h2.this_layer_size, this_layer_size=[10,1])
# import pdb
# pdb.set_trace()
#, 0.1, 0.5, 0.001, 0.005
plots = []
legends = []
batches = []
errors = []
for learning_rate in [0.01, 0.05]:
print 'build the model'
print 'learning rate', learning_rate
mlp = MLP(input_size=input_size,
layers=[h1, output_layer],
train_set=[train_setX, train_sety],
valid_set=[valid_setX, valid_sety],
test_set=[test_set.X, test_set.y],
error_function=loglikehood,
batch_size=20,
learning_rate=learning_rate)
print 'start training'
mlp.train()
#p = plt.plot(mlp.epoch, mlp.valid_error)
#plots.append(p)
batches.append(mlp.epoch)
errors.append(mlp.valid_error)
legends.append(learning_rate)
with open('batches.pkl', 'wb') as bat:
cPickle.dump(batches, bat)
with open('errors.pkl', 'wb') as err:
cPickle.dump(errors, err)
with open('legends.pkl', 'wb') as leg:
cPickle.dump(legends, leg)
