def main(main_params):
"""
Search TODO for those parts you need to complete.
Please follow the step indicated in TODO (step) to complete this script from step = 1 to step = 5.
"""
### set the random seed ###
np.random.seed(int(main_params['random_seed']))
### data processing ###
Xtrain, Ytrain, Xval, Yval, _, _ = data_loader_mnist(
dataset='mnist_subset.json')
N_train, d, _, _ = Xtrain.shape
N_val, _, _, _ = Xval.shape
trainSet = DataSplit(Xtrain, Ytrain)
valSet = DataSplit(Xval, Yval)
### building/defining CNN ###
"""
In this script, we are going to build a CNN for a 10-class classification problem on MNIST.
The network structure is input --> convolution --> relu --> max pooling --> convolution --> relu --> max pooling --> flatten --> dropout --> linear --> softmax_cross_entropy loss
the hidden_layer size (num_L1) is 1225
the output_layer size (num_L2) is 10
"""
model = dict()
num_L1 = 1225
num_L2 = 10
# experimental setup
num_epoch = int(main_params['num_epoch'])
minibatch_size = int(main_params['minibatch_size'])
# optimization setting: _alpha for momentum, _lambda for weight decay
_learning_rate = float(main_params['learning_rate'])
_step = 30
_alpha = float(main_params['alpha'])
_lambda = float(main_params['lambda'])
_dropout_rate = float(main_params['dropout_rate'])
# create objects (modules) from the module classes
model['C1'] = dnn_misc.conv_layer(num_input=d,
num_output=25,
filter_len=5,
stride=1)
model['nonlinear1'] = dnn_misc.relu()
model['M1'] = dnn_misc.max_pool(max_len=2, stride=2)
################################################################################
# TODO (1): Understand the new modules to be included (compared to dnn_cnn.py) #
# You do not need to modify any thing here. #
################################################################################
model['C2'] = dnn_misc.conv_layer(num_input=25,
num_output=25,
filter_len=3,
stride=1)
model['nonlinear2'] = dnn_misc.relu()
model['M2'] = dnn_misc.max_pool(max_len=2, stride=2)
################################################################################
# End of TODO (1) #
################################################################################
model['F1'] = dnn_misc.flatten_layer()
model['drop1'] = dnn_misc.dropout(r=_dropout_rate)
model['L1'] = dnn_misc.linear_layer(input_D=num_L1, output_D=num_L2)
model['loss'] = dnn_misc.softmax_cross_entropy()
# create variables for momentum
if _alpha > 0.0:
momentum = dnn_misc.add_momentum(model)
else:
momentum = None
train_acc_record = []
val_acc_record = []
### run training and validation ###
for t in range(num_epoch):
print('At epoch ' + str(t + 1))
if (t % _step == 0) and (t != 0):
_learning_rate = _learning_rate * 0.1
idx_order = np.random.permutation(N_train)
train_acc = 0.0
train_loss = 0.0
train_count = 0
val_acc = 0.0
val_count = 0
for i in range(int(np.floor(N_train / minibatch_size))):
# get a mini-batch of data
x, y = trainSet.get_example(idx_order[i * minibatch_size:(i + 1) *
minibatch_size])
### forward ###
c1 = model['C1'].forward(x)
h1 = model['nonlinear1'].forward(c1)
m1 = model['M1'].forward(h1)
################################################################################
# TODO (2): Connect the three modules for the forward pass #
# model['C2'] #
# model['nonlinear2'] #
# model['M2'] #
# into the forward pass. #
# Please make sure to connect them with m1 and m2, the input and output of the #
# previous and the next modules, respectively #
################################################################################
# TODO (2)
c2 = model['C2'].forward(m1)
h2 = model['nonlinear2'].forward(c2)
m2 = model['M2'].forward(h2)
################################################################################
# End of TODO (2) #
################################################################################
f1 = model['F1'].forward(m2)
d1 = model['drop1'].forward(f1, is_train=True)
a1 = model['L1'].forward(d1)
loss = model['loss'].forward(a1, y)
### backward ###
grad_a1 = model['loss'].backward(a1, y)
grad_d1 = model['L1'].backward(d1, grad_a1)
grad_f1 = model['drop1'].backward(f1, grad_d1)
grad_m2 = model['F1'].backward(m2, grad_f1)
################################################################################
# TODO (3): Connect the three modules for the backward pass #
# model['C2'] #
# model['nonlinear2'] #
# model['M2'] #
# into the backward pass. #
# Please make sure to connect them with grad_m2 and grad_m1, the input and #
# output of the previous and the next modules, respectively #
# Please pay attention to the number of arguments in the backward pass. #
################################################################################
# TODO (3)
grad_h2 = model['M2'].backward(h2, grad_m2)
grad_c2 = model['nonlinear2'].backward(c2, grad_h2)
grad_m1 = model['C2'].backward(m1, grad_c2)
################################################################################
# End of TODO (3) #
################################################################################
grad_h1 = model['M1'].backward(h1, grad_m1)
grad_c1 = model['nonlinear1'].backward(c1, grad_h1)
grad_x = model['C1'].backward(x, grad_c1)
### gradient_update ###
for module_name, module in model.items():
# check if a module has learnable parameters
if hasattr(module, 'params'):
for key, _ in module.params.items():
g = module.gradient[key] + _lambda * module.params[key]
if _alpha > 0.0:
momentum[module_name + '_' +
key] = _alpha * momentum[
module_name + '_' +
key] - _learning_rate * g
module.params[key] += momentum[module_name + '_' +
key]
else:
module.params[key] -= _learning_rate * g
### Computing training accuracy and obj ###
for i in range(int(np.floor(N_train / minibatch_size))):
x, y = trainSet.get_example(
np.arange(i * minibatch_size, (i + 1) * minibatch_size))
### forward ###
c1 = model['C1'].forward(x)
h1 = model['nonlinear1'].forward(c1)
m1 = model['M1'].forward(h1)
################################################################################
# TODO (4): Connect the three modules for the forward pass #
# model['C2'] #
# model['nonlinear2'] #
# model['M2'] #
# into the forward pass. #
# Please make sure to connect them with m1 and m2, the input and output of the #
# previous and the next modules, respectively #
################################################################################
# TODO (4)
c2 = model['C2'].forward(m1)
h2 = model['nonlinear2'].forward(c2)
m2 = model['M2'].forward(h2)
################################################################################
# End of TODO (4) #
################################################################################
f1 = model['F1'].forward(m2)
d1 = model['drop1'].forward(f1, is_train=False)
a1 = model['L1'].forward(d1)
loss = model['loss'].forward(a1, y)
train_loss += len(y) * loss
train_acc += np.sum(predict_label(a1) == y)
train_count += len(y)
train_loss = train_loss / train_count
train_acc = train_acc / train_count
train_acc_record.append(train_acc)
print('Training loss at epoch ' + str(t + 1) + ' is ' +
str(train_loss))
print('Training accuracy at epoch ' + str(t + 1) + ' is ' +
str(train_acc))
### Computing validation accuracy ###
for i in range(int(np.floor(N_val / minibatch_size))):
x, y = valSet.get_example(
np.arange(i * minibatch_size, (i + 1) * minibatch_size))
### forward ###
c1 = model['C1'].forward(x)
h1 = model['nonlinear1'].forward(c1)
m1 = model['M1'].forward(h1)
################################################################################
# TODO (5): Connect the three modules for the forward pass #
# model['C2'] #
# model['nonlinear2'] #
# model['M2'] #
# into the forward pass. #
# Please make sure to connect them with m1 and m2, the input and output of the #
# previous and the next modules, respectively #
################################################################################
# TODO (5)
c2 = model['C2'].forward(m1)
h2 = model['nonlinear2'].forward(c2)
m2 = model['M2'].forward(h2)
################################################################################
# End of TODO (5) #
################################################################################
f1 = model['F1'].forward(m2)
d1 = model['drop1'].forward(f1, is_train=False)
a1 = model['L1'].forward(d1)
val_acc += np.sum(predict_label(a1) == y)
val_count += len(y)
val_acc = val_acc / val_count
val_acc_record.append(val_acc)
print('Validation accuracy at epoch ' + str(t + 1) + ' is ' +
str(val_acc))
# save file
json.dump(
{
'train': train_acc_record,
'val': val_acc_record
},
open(
'CNN2_lr' + str(main_params['learning_rate']) + '_m' +
str(main_params['alpha']) + '_w' + str(main_params['lambda']) +
'_d' + str(main_params['dropout_rate']) + '.json', 'w'))
print('Finish running!')
return
def main(main_params):
### set the random seed ###
np.random.seed(int(main_params['random_seed']))
### data processing ###
Xtrain, Ytrain, Xval, Yval, _, _ = data_loader_mnist(
dataset='mnist_subset.json')
N_train, d = Xtrain.shape
N_val, _ = Xval.shape
trainSet = DataSplit(Xtrain, Ytrain)
valSet = DataSplit(Xval, Yval)
### building/defining MLP ###
"""
MLP for a 10-class classification problem on MNIST.
The network structure is input --> linear --> relu --> dropout --> linear --> softmax_cross_entropy loss
the hidden_layer size (num_L1) is 1000
the output_layer size (num_L2) is 10
"""
model = dict()
num_L1 = 1000
num_L2 = 10
# experimental setup
num_epoch = int(main_params['num_epoch'])
minibatch_size = int(main_params['minibatch_size'])
# optimization setting: _alpha for momentum, _lambda for weight decay
_learning_rate = float(main_params['learning_rate'])
_step = 10
_alpha = float(main_params['alpha'])
_lambda = float(main_params['lambda'])
_dropout_rate = float(main_params['dropout_rate'])
# create objects (modules) from the module classes
model['L1'] = dnn_misc.linear_layer(input_D=d, output_D=num_L1)
model['nonlinear1'] = dnn_misc.relu()
model['drop1'] = dnn_misc.dropout(r=_dropout_rate)
model['L2'] = dnn_misc.linear_layer(input_D=num_L1, output_D=num_L2)
model['loss'] = dnn_misc.softmax_cross_entropy()
# create variables for momentum
if _alpha > 0.0:
momentum = dnn_misc.add_momentum(model)
else:
momentum = None
train_acc_record = []
val_acc_record = []
### run training and validation ###
for t in range(num_epoch):
print('At epoch ' + str(t + 1))
if (t % _step == 0) and (t != 0):
_learning_rate = _learning_rate * 0.1
idx_order = np.random.permutation(N_train)
train_acc = 0.0
train_loss = 0.0
train_count = 0
val_acc = 0.0
val_count = 0
for i in range(int(np.floor(N_train / minibatch_size))):
# get a mini-batch of data
x, y = trainSet.get_example(idx_order[i * minibatch_size:(i + 1) *
minibatch_size])
### forward ###
a1 = model['L1'].forward(x)
h1 = model['nonlinear1'].forward(a1)
d1 = model['drop1'].forward(h1, is_train=True)
a2 = model['L2'].forward(d1)
loss = model['loss'].forward(a2, y)
### backward ###
grad_a2 = model['loss'].backward(a2, y)
grad_d1 = model['L2'].backward(d1, grad_a2)
grad_h1 = model['drop1'].backward(h1, grad_d1)
grad_a1 = model['nonlinear1'].backward(a1, grad_h1)
grad_x = model['L1'].backward(x, grad_a1)
### gradient_update ###
for module_name, module in model.items():
# check if a module has learnable parameters
if hasattr(module, 'params'):
for key, _ in module.params.items():
g = module.gradient[key] + _lambda * module.params[key]
if _alpha > 0.0:
momentum[module_name + '_' +
key] = _alpha * momentum[
module_name + '_' +
key] - _learning_rate * g
module.params[key] += momentum[module_name + '_' +
key]
else:
module.params[key] -= _learning_rate * g
### Computing training accuracy and obj ###
for i in range(int(np.floor(N_train / minibatch_size))):
x, y = trainSet.get_example(
np.arange(i * minibatch_size, (i + 1) * minibatch_size))
### forward ###
a1 = model['L1'].forward(x)
h1 = model['nonlinear1'].forward(a1)
d1 = model['drop1'].forward(h1, is_train=False)
a2 = model['L2'].forward(d1)
loss = model['loss'].forward(a2, y)
train_loss += len(y) * loss
train_acc += np.sum(predict_label(a2) == y)
train_count += len(y)
train_loss = train_loss / train_count
train_acc = train_acc / train_count
train_acc_record.append(train_acc)
print('Training loss at epoch ' + str(t + 1) + ' is ' +
str(train_loss))
print('Training accuracy at epoch ' + str(t + 1) + ' is ' +
str(train_acc))
### Computing validation accuracy ###
for i in range(int(np.floor(N_val / minibatch_size))):
x, y = valSet.get_example(
np.arange(i * minibatch_size, (i + 1) * minibatch_size))
### forward ###
a1 = model['L1'].forward(x)
h1 = model['nonlinear1'].forward(a1)
d1 = model['drop1'].forward(h1, is_train=False)
a2 = model['L2'].forward(d1)
val_acc += np.sum(predict_label(a2) == y)
val_count += len(y)
val_acc = val_acc / val_count
val_acc_record.append(val_acc)
print('Validation accuracy at epoch ' + str(t + 1) + ' is ' +
str(val_acc))
# save file
json.dump(
{
'train': train_acc_record,
'val': val_acc_record
},
open(
'MLP_lr' + str(main_params['learning_rate']) + '_m' +
str(main_params['alpha']) + '_w' + str(main_params['lambda']) +
'_d' + str(main_params['dropout_rate']) + '.json', 'w'))
print('Finish running!')
return
def main(main_params):
### set the random seed ###
np.random.seed(int(main_params['random_seed']))
### data processing ###
Xtrain, Ytrain, Xval, Yval, Xtest, Ytest = data_loader_mnist(
dataset='mnist_subset.json')
N_train, d = Xtrain.shape
N_val, _ = Xval.shape
N_test, _ = Xtest.shape
trainSet = DataSplit(Xtrain, Ytrain)
valSet = DataSplit(Xval, Yval)
testSet = DataSplit(Xtest, Ytest)
### building/defining MLP ###
"""
In this script, we are going to build an MLP for a 10-class classification problem on MNIST.
The network structure is input --> linear --> relu --> dropout --> linear --> softmax_cross_entropy loss
the hidden_layer size (num_L1) is 1000
the output_layer size (num_L2) is 10
"""
model = dict()
num_L1 = 1000
num_L2 = 10
# experimental setup
num_epoch = int(main_params['num_epoch'])
minibatch_size = int(main_params['minibatch_size'])
# optimization setting: _alpha for momentum, _lambda for weight decay
_learning_rate = float(main_params['learning_rate'])
_step = 10
_alpha = 0.0
_lambda = float(main_params['lambda'])
_optimizer = main_params['optim']
_epsilon = main_params['epsilon']
# create objects (modules) from the module classes
model['L1'] = dnn_misc.linear_layer(input_D=d, output_D=num_L1)
model['nonlinear1'] = dnn_misc.relu()
model['L2'] = dnn_misc.linear_layer(input_D=num_L1, output_D=num_L2)
model['loss'] = dnn_misc.softmax_cross_entropy()
# create variables for momentum
if _optimizer == "Gradient_Descent_Momentum":
# creates a dictionary that holds the value of momentum for learnable parameters
momentum = dnn_misc.add_momentum(model)
_alpha = 0.9
else:
momentum = None
train_acc_record = []
val_acc_record = []
train_loss_record = []
val_loss_record = []
### run training and validation ###
for t in range(num_epoch):
print('At epoch ' + str(t + 1))
if (t % _step == 0) and (t != 0):
# learning_rate decay
_learning_rate = _learning_rate * 0.1
# shuffle the train data
idx_order = np.random.permutation(N_train)
for i in range(int(np.floor(N_train / minibatch_size))):
# get a mini-batch of data
x, y = trainSet.get_example(idx_order[i * minibatch_size:(i + 1) *
minibatch_size])
### forward ###
a1 = model['L1'].forward(x)
h1 = model['nonlinear1'].forward(a1)
a2 = model['L2'].forward(h1)
loss = model['loss'].forward(a2, y)
### backward ###
grad_a2 = model['loss'].backward(a2, y)
grad_h1 = model['L2'].backward(h1, grad_a2)
grad_a1 = model['nonlinear1'].backward(a1, grad_h1)
grad_x = model['L1'].backward(x, grad_a1)
### gradient_update ###
for module_name, module in model.items():
# model is a dictionary with 'L1', 'L2', 'nonLinear1' and 'loss' as keys.
# the values for these keys are the corresponding objects created in line 123-126 using classes
# defined in dnn_misc.py
# check if the module has learnable parameters. not all modules have learnable parameters.
# if it does, the module object will have an attribute called 'params'. See Linear Layer for more details.
if hasattr(module, 'params'):
for key, _ in module.params.items():
# gradient computed during the backward pass + L2 regularization term
# _lambda is the regularization hyper parameter
g = module.gradient[key] + _lambda * module.params[key]
if _optimizer == "Minibatch_Gradient_Descent":
################################################################################
# TODO: Write the gradient update for the module parameter. #
# module.params[key] has to be updated with the new value. #
# parameter update will be of the form: w = w - learning_rate * dl/dw #
################################################################################
#
module.params[key] -= _learning_rate * g
elif _optimizer == "Gradient_Descent_Momentum":
################################################################################
# TODO: Understand how the update differs when we use momentum. #
# module.params[key] has to be updated with the new value. #
# momentum(w) = _aplha * momemtum(w) at previous step + _learning_rate * g #
# parameter update will be of the form: w = w - momentum(w) #
################################################################################
parameter = module_name + '_' + key
momentum[parameter] = _alpha * momentum[
parameter] + _learning_rate * g
module.params[key] -= momentum[parameter]
### Compute train accuracy ###
train_acc = 0.0
train_loss = 0.0
train_count = 0
for i in range(int(np.floor(N_train / minibatch_size))):
x, y = trainSet.get_example(
np.arange(minibatch_size * i, minibatch_size * (i + 1)))
### forward ###
a1 = model['L1'].forward(x)
h1 = model['nonlinear1'].forward(a1)
a2 = model['L2'].forward(h1)
loss = model['loss'].forward(a2, y)
train_loss += len(y) * loss
train_acc += np.sum(predict_label(a2) == y)
train_count += len(y)
train_loss = train_loss / train_count
train_acc = train_acc / train_count
train_acc_record.append(train_acc)
train_loss_record.append(train_loss)
print('Training loss at epoch ' + str(t + 1) + ' is ' +
str(train_loss))
print('Training accuracy at epoch ' + str(t + 1) + ' is ' +
str(train_acc))
### Compute validation accuracy ###
val_acc = 0.0
val_loss = 0.0
val_count = 0
for i in range(int(np.floor(N_val / minibatch_size))):
x, y = valSet.get_example(
np.arange(minibatch_size * i, minibatch_size * (i + 1)))
### forward ###
a1 = model['L1'].forward(x)
h1 = model['nonlinear1'].forward(a1)
a2 = model['L2'].forward(h1)
loss = model['loss'].forward(a2, y)
val_loss += len(y) * loss
val_acc += np.sum(predict_label(a2) == y)
val_count += len(y)
val_loss = val_loss / val_count
val_acc = val_acc / val_count
val_acc_record.append(val_acc)
val_loss_record.append(val_loss)
print('Validation loss at epoch ' + str(t + 1) + ' is ' +
str(val_loss))
print('Validation accuracy at epoch ' + str(t + 1) + ' is ' +
str(val_acc))
### Compute test accuracy ###
################################################################################
# TODO: Do a forward pass on test data and compute test accuracy and loss. #
# populate them in test_loss and test_acc. #
################################################################################
test_loss = 0.0
test_acc = 0.0
test_count = 0
for i in range(int(np.floor(N_test / minibatch_size))):
x, y = testSet.get_example(
np.arange(minibatch_size * i, minibatch_size * (i + 1)))
### forward ###
a1 = model['L1'].forward(x)
h1 = model['nonlinear1'].forward(a1)
a2 = model['L2'].forward(h1)
loss = model['loss'].forward(a2, y)
test_loss += len(y) * loss
test_acc += np.sum(predict_label(a2) == y)
test_count += len(y)
test_loss = test_loss / test_count
test_acc = test_acc / test_count
print('Test loss at epoch ' + str(t + 1) + ' is ' + str(test_loss))
print('Test accuracy at epoch ' + str(t + 1) + ' is ' + str(test_acc))
plot_metrics(num_epoch, train_acc_record, val_acc_record,
train_loss_record, val_loss_record, _optimizer)
# save file
json.dump(
{
'train_accuracy': train_acc_record,
'train_loss': train_loss_record,
'val_accuracy': val_acc_record,
'val_loss': val_loss_record,
'test_accuracy': test_acc,
'test_loss': test_loss
},
open(
_optimizer + '_lr' + str(main_params['learning_rate']) + '_m' +
str(_alpha) + '_w' + str(main_params['lambda']) + '.json', 'w'))
# plotting to understand what the network is trying to do
# plot raw mnist data
plot_tSNE(Xtest, Ytest, 't-SNE raw MNIST.png')
################################################################################
# TODO: Vizualize the ouput of the first and second layer of the neural network#
# on test data using t-SNE. Populate arrays 'first_layer_out' and #
# and 'second_layer_out'. #
################################################################################
# first_layer_out = output of the neural network first layer on test data
first_layer_out = np.zeros((N_test, num_L1), dtype=float)
# second_layer_out = output of the neural network second layer on test data
second_layer_out = np.zeros((N_test, num_L2), dtype=float)
# Add your code here
first_layer_out = model['L1'].forward(testSet.X)
h1 = model['nonlinear1'].forward(first_layer_out)
second_layer_out = model['L2'].forward(h1)
###########################################################################
# Please DO NOT change the following parts of the script #
###########################################################################
plot_tSNE(first_layer_out, Ytest, _optimizer + '_t-SNE_1.png')
plot_tSNE(second_layer_out, Ytest, _optimizer + '_t-SNE_2.png')
print('Finish running!')
