def contractive_denoising_autoencoder(input_var=None):
"""Contractive Denoising Autoencoder"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 20)
hp('n_epochs', 1000)
hp('learning_rate', 0.01)
hp('patience', 10000)
# Unsupervised hyperparameters
hp_ae = Hyperparameters()
hp_ae('batch_size', hp.batch_size)
hp_ae('n_epochs', 15)
hp_ae('learning_rate', 0.01)
# Create connected layers
# Input layer
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
# Auto Encoder Layer
l_ae1 = AutoEncoder(incoming=l_in, nb_units=500, hyperparameters=hp_ae, contraction_level=0.3,
activation=relu, name='Contractive Denoising AutoEncoder')
# Logistic regression Layer
l_out = LogisticRegression(incoming=l_ae1, nb_class=10, name='Logistic regression')
# Create network and add layers
net = Network('contractive_denoising_autoencoder')
net.add(l_in)
net.add(l_ae1)
net.add(l_out)
return net, hp
def mlp(input_var=None):
"""Multi Layer Perceptron"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 20)
hp('n_epochs', 1000)
hp('learning_rate', 0.01)
hp('l1_reg', 0.00)
hp('l2_reg', 0.0001)
hp('patience', 5000)
# Create connected layers
# Input layer
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
# Dense Layer
l_hid1 = DenseLayer(incoming=l_in, nb_units=500, W=glorot_uniform, l1=hp.l1_reg,
l2=hp.l2_reg, activation=tanh, name='Hidden layer 1')
# Dense Layer
l_hid2 = DenseLayer(incoming=l_hid1, nb_units=500, W=glorot_uniform, l1=hp.l1_reg,
l2=hp.l2_reg, activation=tanh, name='Hidden layer 2')
# Logistic regression Layer
l_out = LogisticRegression(incoming=l_hid2, nb_class=10, l1=hp.l1_reg,
l2=hp.l2_reg, name='Logistic regression')
# Create network and add layers
net = Network('mlp')
net.add(l_in)
net.add(l_hid1)
net.add(l_hid2)
net.add(l_out)
return net, hp
def dbn(input_var=None):
"""Deep Belief Network"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 10)
hp('n_epochs', 1000)
hp('learning_rate', 0.1)
hp('patience', 100)
# Unsupervised hyperparameters
hp_ae = Hyperparameters()
hp_ae('batch_size', hp.batch_size)
hp_ae('n_epochs', 15)
hp_ae('learning_rate', 0.01)
# Create connected layers
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
l_rbm1 = RBM(incoming=l_in, nb_units=500, hyperparameters=hp_ae,
name='Restricted Boltzmann Machine 1')
l_rbm2 = RBM(incoming=l_rbm1, nb_units=500, hyperparameters=hp_ae,
name='Restricted Boltzmann Machine 2')
l_out = LogisticRegression(incoming=l_rbm2, nb_class=10, name='Logistic regression')
# Create network and add layers
net = Network('dbn')
net.add(l_in)
net.add(l_rbm1)
net.add(l_rbm2)
net.add(l_out)
return net, hp
def convpool(input_var=None):
"""Convolution and MaxPooling"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 500)
hp('n_epochs', 200)
hp('learning_rate', 0.01)
hp('patience', 10000)
# Create connected layers
image_shape = (None, 1, 28, 28) # (batch size, nb input feature maps, image height, image width)
filter_shape = (20, 1, 5, 5) # (number of filters, nb input feature maps, filter height, filter width)
poolsize = (2, 2) # downsampling factor per (row, col)
# Input layer
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
# ConvLayer needs 4D Tensor
l_rs = ReshapeLayer(incoming=l_in, output_shape=image_shape)
# ConvPool Layer
l_cp = ConvPoolLayer(incoming=l_rs, poolsize=poolsize, image_shape=image_shape,
filter_shape=filter_shape, name='ConvPool layer')
# flatten convpool output
l_fl = FlattenLayer(incoming=l_cp, ndim=2)
# Logistic regression Layer
l_out = LogisticRegression(incoming=l_fl, nb_class=10, name='Logistic regression')
# Create network and add layers
net = Network('convpool')
net.add(l_in)
net.add(l_rs)
net.add(l_cp)
net.add(l_out)
return net, hp
def dropconnect(input_var=None):
"""DropConnect MLP"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 20)
hp('n_epochs', 1000)
hp('learning_rate', 0.01)
hp('patience', 10000)
# Create connected layers
# Input layer
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
# DropConnect Layer
l_dc1 = Dropconnect(incoming=l_in, nb_units=500, corruption_level=0.4,
W=glorot_uniform, activation=relu, name='Dropconnect layer 1')
# DropConnect Layer
l_dc2 = Dropconnect(incoming=l_dc1, nb_units=500, corruption_level=0.2,
W=glorot_uniform, activation=relu, name='Dropconnect layer 2')
# Logistic regression Layer
l_out = LogisticRegression(incoming=l_dc2, nb_class=10, name='Logistic regression')
# Create network and add layers
net = Network('dropconnect')
net.add(l_in)
net.add(l_dc1)
net.add(l_dc2)
net.add(l_out)
return net, hp
def grid_search():
for hp in hps:
# create the model
model = Model(name='mlp grid search', data=data)
# add the hyperparameters to the model
model.hp = hp
# Create connected layers
# Input layer
l_in = InputLayer(shape=(None, 28 * 28), name='Input')
# Dense Layer 1
l_hid1 = DenseLayer(incoming=l_in, nb_units=5, W=hp.initialisation, l1=hp.l1_reg,
l2=hp.l2_reg, activation=hp.activation, name='Hidden layer 1')
# Dense Layer 2
l_hid2 = DenseLayer(incoming=l_hid1, nb_units=5, W=hp.initialisation, l1=hp.l1_reg,
l2=hp.l2_reg, activation=hp.activation, name='Hidden layer 2')
# Logistic regression Layer
l_out = LogisticRegression(incoming=l_hid2, nb_class=10, l1=hp.l1_reg,
l2=hp.l2_reg, name='Logistic regression')
# Create network and add layers
net = Network('mlp')
net.add(l_in)
net.add(l_hid1)
net.add(l_hid2)
net.add(l_out)
# add the network to the model
model.network = net
# updates method
model.updates = yadll.updates.sgd
reports.append((hp, model.train()))
with open('reports.pkl', 'wb') as report_file:
cPickle.dump(reports, report_file)
def rnn(input_var=None):
"""Recurrent Neural Network"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 500)
hp('n_epochs', 1000)
hp('learning_rate', 0.1)
hp('patience', 500)
# Create connected layers
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
l_rnn = RNN(incoming=l_in, n_hidden=100, n_out=28 * 28, name='Recurrent Neural Network')
l_out = LogisticRegression(incoming=l_rnn, nb_class=10, name='Logistic regression')
# Create network and add layers
net = Network('rnn')
net.add(l_in)
net.add(l_rnn)
net.add(l_out)
return net, hp
def logistic_regression(input_var=None):
"""Logistic Regression"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 600)
hp('n_epochs', 1000)
hp('learning_rate', 0.1)
hp('patience', 5000)
# Create connected layers
# Input layer
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
# Logistic regression Layer
l_out = LogisticRegression(incoming=l_in, nb_class=10, name='Logistic regression')
# Create network and add layers
net = Network('logistic_regression')
net.add(l_in)
net.add(l_out)
return net, hp
def lstm(input_var=None):
"""Long Short Term Memory"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 500)
hp('n_epochs', 1000)
hp('learning_rate', 0.1)
hp('patience', 500)
# Create connected layers
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
l_lstm = LSTM(incoming=l_in, n_hidden=100, n_out=28 * 28, name='Long Short Term Memory')
l_out = LogisticRegression(incoming=l_lstm, nb_class=10, name='Logistic regression')
# Create network and add layers
net = Network('lstm')
net.add(l_in)
net.add(l_lstm)
net.add(l_out)
return net, hp
def lenet5(input_var=None):
"""LeNet-5"""
# Hyperparameters
hp = Hyperparameters()
hp('batch_size', 500)
hp('n_epochs', 200)
hp('learning_rate', 0.1)
hp('patience', 10000)
# Create connected layers
# Input layer
l_in = InputLayer(shape=(None, 28 * 28), input_var=input_var, name='Input')
# ConvLayer needs 4D Tensor
image_shape = (None, 1, 28, 28)
l_rs = ReshapeLayer(incoming=l_in, output_shape=image_shape)
# first convpool
filter_shape = (20, 1, 5, 5)
poolsize = (2, 2)
l_cp1 = ConvPoolLayer(incoming=l_rs, poolsize=poolsize, image_shape=image_shape,
filter_shape=filter_shape, name='ConvPool layer 1')
# second convpool
image_shape = (None, 20, 12, 12) # (batch size, nb filters, (28-5)/2, (28-5)/2)
filter_shape = (50, 20, 5, 5)
poolsize = (2, 2)
l_cp2 = ConvPoolLayer(incoming=l_cp1, poolsize=poolsize, image_shape=image_shape,
filter_shape=filter_shape, name='ConvPool layer 2')
# flatten convpool output
l_fl = FlattenLayer(incoming=l_cp2, ndim=2)
# Dense Layer
l_hid1 = DenseLayer(incoming=l_fl, nb_units=500, W=glorot_uniform, activation=tanh, name='Hidden layer 1')
# Logistic regression Layer
l_out = LogisticRegression(incoming=l_hid1, nb_class=10, name='Logistic regression')
# Create network and add layers
net = Network('convpool')
net.add(l_in)
net.add(l_rs)
net.add(l_cp1)
net.add(l_cp2)
net.add(l_fl)
net.add(l_hid1)
net.add(l_out)
return net, hp
