Model Improvement: PyTorch

batch_norm.py

torch.manual_seed(0)

class Net(Module):   
    def __init__(self):
        super(Net, self).__init__()

        self.cnn_layers = Sequential(
            # Defining a 2D convolution layer
            Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),
            # batch normalization layer
            BatchNorm2d(16),
            MaxPool2d(kernel_size=2, stride=2),
            # Defining another 2D convolution layer
            Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),    
            # batch normalization layer
            BatchNorm2d(32),
            MaxPool2d(kernel_size=2, stride=2),
        )

        self.linear_layers = Sequential(
            Linear(32 * 56 * 56, 2)
        )

    # Defining the forward pass    
    def forward(self, x):
        x = self.cnn_layers(x)
        x = x.view(x.size(0), -1)
        x = self.linear_layers(x)
        return x

batch_norm_parameter.py

# defining the model
model = Net()
# defining the optimizer
optimizer = Adam(model.parameters(), lr=0.00005)
# defining the loss function
criterion = CrossEntropyLoss()
# checking if GPU is available
if torch.cuda.is_available():
    model = model.cuda()
    criterion = criterion.cuda()

print(model)

batch_norm_train.py

torch.manual_seed(0)

# batch size of the model
batch_size = 128

# number of epochs to train the model
n_epochs = 5

for epoch in range(1, n_epochs+1):

    # keep track of training and validation loss
    train_loss = 0.0
        
    permutation = torch.randperm(train_x.size()[0])

    training_loss = []
    for i in tqdm(range(0,train_x.size()[0], batch_size)):

        indices = permutation[i:i+batch_size]
        batch_x, batch_y = train_x[indices], train_y[indices]
        
        if torch.cuda.is_available():
            batch_x, batch_y = batch_x.cuda(), batch_y.cuda()
        
        optimizer.zero_grad()
        # in case you wanted a semi-full example
        outputs = model(batch_x)
        loss = criterion(outputs,batch_y)

        training_loss.append(loss.item())
        loss.backward()
        optimizer.step()
        
    training_loss = np.average(training_loss)
    print('epoch: \t', epoch, '\t training loss: \t', training_loss)

batch_norm_train_acc.py

# prediction for training set
prediction = []
target = []
permutation = torch.randperm(train_x.size()[0])
for i in tqdm(range(0,train_x.size()[0], batch_size)):
    indices = permutation[i:i+batch_size]
    batch_x, batch_y = train_x[indices], train_y[indices]

    if torch.cuda.is_available():
        batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    with torch.no_grad():
        output = model(batch_x.cuda())

    softmax = torch.exp(output).cpu()
    prob = list(softmax.numpy())
    predictions = np.argmax(prob, axis=1)
    prediction.append(predictions)
    target.append(batch_y)
    
# training accuracy
accuracy = []
for i in range(len(prediction)):
    accuracy.append(accuracy_score(target[i],prediction[i]))
    
print('training accuracy: \t', np.average(accuracy))

batch_norm_valid_acc.py

# prediction for validation set
prediction_val = []
target_val = []
permutation = torch.randperm(val_x.size()[0])
for i in tqdm(range(0,val_x.size()[0], batch_size)):
    indices = permutation[i:i+batch_size]
    batch_x, batch_y = val_x[indices], val_y[indices]

    if torch.cuda.is_available():
        batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    with torch.no_grad():
        output = model(batch_x.cuda())

    softmax = torch.exp(output).cpu()
    prob = list(softmax.numpy())
    predictions = np.argmax(prob, axis=1)
    prediction_val.append(predictions)
    target_val.append(batch_y)
    
# validation accuracy
accuracy_val = []
for i in range(len(prediction_val)):
    accuracy_val.append(accuracy_score(target_val[i],prediction_val[i]))
    
print('validation accuracy: \t', np.average(accuracy_val))

cnn.py

torch.manual_seed(0)

class Net(Module):   
    def __init__(self):
        super(Net, self).__init__()

        self.cnn_layers = Sequential(
            # Defining a 2D convolution layer
            Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),
            MaxPool2d(kernel_size=2, stride=2),
            # Defining another 2D convolution layer
            Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),
            MaxPool2d(kernel_size=2, stride=2)
        )

        self.linear_layers = Sequential(
            Linear(32 * 56 * 56, 2)
        )

    # Defining the forward pass    
    def forward(self, x):
        x = self.cnn_layers(x)
        x = x.view(x.size(0), -1)
        x = self.linear_layers(x)
        return x

cnn_train.py

torch.manual_seed(0)

# batch size of the model
batch_size = 128

# number of epochs to train the model
n_epochs = 25

for epoch in range(1, n_epochs+1):

    # keep track of training and validation loss
    train_loss = 0.0
        
    permutation = torch.randperm(train_x.size()[0])

    training_loss = []
    for i in tqdm(range(0,train_x.size()[0], batch_size)):

        indices = permutation[i:i+batch_size]
        batch_x, batch_y = train_x[indices], train_y[indices]
        
        if torch.cuda.is_available():
            batch_x, batch_y = batch_x.cuda(), batch_y.cuda()
        
        optimizer.zero_grad()
        # in case you wanted a semi-full example
        outputs = model(batch_x)
        loss = criterion(outputs,batch_y)

        training_loss.append(loss.item())
        loss.backward()
        optimizer.step()
        
    training_loss = np.average(training_loss)
    print('epoch: \t', epoch, '\t training loss: \t', training_loss)

cnn_train_acc.py

# prediction for training set
prediction = []
target = []
permutation = torch.randperm(train_x.size()[0])
for i in tqdm(range(0,train_x.size()[0], batch_size)):
    indices = permutation[i:i+batch_size]
    batch_x, batch_y = train_x[indices], train_y[indices]

    if torch.cuda.is_available():
        batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    with torch.no_grad():
        output = model(batch_x.cuda())

    softmax = torch.exp(output).cpu()
    prob = list(softmax.numpy())
    predictions = np.argmax(prob, axis=1)
    prediction.append(predictions)
    target.append(batch_y)
    
# training accuracy
accuracy = []
for i in range(len(prediction)):
    accuracy.append(accuracy_score(target[i],prediction[i]))
    
print('training accuracy: \t', np.average(accuracy))

cnn_valid_acc.py

# prediction for validation set
prediction_val = []
target_val = []
permutation = torch.randperm(val_x.size()[0])
for i in tqdm(range(0,val_x.size()[0], batch_size)):
    indices = permutation[i:i+batch_size]
    batch_x, batch_y = val_x[indices], val_y[indices]

    if torch.cuda.is_available():
        batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    with torch.no_grad():
        output = model(batch_x.cuda())

    softmax = torch.exp(output).cpu()
    prob = list(softmax.numpy())
    predictions = np.argmax(prob, axis=1)
    prediction_val.append(predictions)
    target_val.append(batch_y)
    
# validation accuracy
accuracy_val = []
for i in range(len(prediction_val)):
    accuracy_val.append(accuracy_score(target_val[i],prediction_val[i]))
    
print('validation accuracy: \t', np.average(accuracy_val))

combined_parameter.py

# defining the model
model = Net()
# defining the optimizer
optimizer = Adam(model.parameters(), lr=0.00025)
# defining the loss function
criterion = CrossEntropyLoss()
# checking if GPU is available
if torch.cuda.is_available():
    model = model.cuda()
    criterion = criterion.cuda()

print(model)

combined_train.py

torch.manual_seed(0)

# batch size of the model
batch_size = 128

# number of epochs to train the model
n_epochs = 10

for epoch in range(1, n_epochs+1):

    # keep track of training and validation loss
    train_loss = 0.0
        
    permutation = torch.randperm(train_x.size()[0])

    training_loss = []
    for i in tqdm(range(0,train_x.size()[0], batch_size)):

        indices = permutation[i:i+batch_size]
        batch_x, batch_y = train_x[indices], train_y[indices]
        
        if torch.cuda.is_available():
            batch_x, batch_y = batch_x.cuda(), batch_y.cuda()
        
        optimizer.zero_grad()
        # in case you wanted a semi-full example
        outputs = model(batch_x)
        loss = criterion(outputs,batch_y)

        training_loss.append(loss.item())
        loss.backward()
        optimizer.step()
        
    training_loss = np.average(training_loss)
    print('epoch: \t', epoch, '\t training loss: \t', training_loss)

combined_train_acc.py

# prediction for training set
prediction = []
target = []
permutation = torch.randperm(train_x.size()[0])
for i in tqdm(range(0,train_x.size()[0], batch_size)):
    indices = permutation[i:i+batch_size]
    batch_x, batch_y = train_x[indices], train_y[indices]

    if torch.cuda.is_available():
        batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    with torch.no_grad():
        output = model(batch_x.cuda())

    softmax = torch.exp(output).cpu()
    prob = list(softmax.numpy())
    predictions = np.argmax(prob, axis=1)
    prediction.append(predictions)
    target.append(batch_y)
    
# training accuracy
accuracy = []
for i in range(len(prediction)):
    accuracy.append(accuracy_score(target[i],prediction[i]))
    
print('training accuracy: \t', np.average(accuracy))

combined_valid_acc.py

# prediction for validation set
prediction_val = []
target_val = []
permutation = torch.randperm(val_x.size()[0])
for i in tqdm(range(0,val_x.size()[0], batch_size)):
    indices = permutation[i:i+batch_size]
    batch_x, batch_y = val_x[indices], val_y[indices]

    if torch.cuda.is_available():
        batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    with torch.no_grad():
        output = model(batch_x.cuda())

    softmax = torch.exp(output).cpu()
    prob = list(softmax.numpy())
    predictions = np.argmax(prob, axis=1)
    prediction_val.append(predictions)
    target_val.append(batch_y)
    
# validation accuracy
accuracy_val = []
for i in range(len(prediction_val)):
    accuracy_val.append(accuracy_score(target_val[i],prediction_val[i]))
    
print('validation accuracy: \t', np.average(accuracy_val))

dataset.py

# loading dataset
train = pd.read_csv('emergency_train.csv')

# loading training images
train_img = []
for img_name in tqdm(train['image_names']):
    # defining the image path
    image_path = '../Hack Session/images/' + img_name
    # reading the image
    img = imread(image_path)
    # normalizing the pixel values
    img = img/255
    # resizing the image to (224,224,3)
    img = resize(img, output_shape=(224,224,3), mode='constant', anti_aliasing=True)
    # converting the type of pixel to float 32
    img = img.astype('float32')
    # appending the image into the list
    train_img.append(img)

# converting the list to numpy array
train_x = np.array(train_img)
train_x.shape

dropout.py

torch.manual_seed(0)

class Net(Module):   
    def __init__(self):
        super(Net, self).__init__()

        self.cnn_layers = Sequential(
            # Defining a 2D convolution layer
            Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),
            MaxPool2d(kernel_size=2, stride=2),
            # dropout layer
            Dropout(),
            # Defining another 2D convolution layer
            Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),
            MaxPool2d(kernel_size=2, stride=2),
            # dropout layer
            Dropout(),
        )

        self.linear_layers = Sequential(
            Linear(32 * 56 * 56, 2)
        )

    # Defining the forward pass    
    def forward(self, x):
        x = self.cnn_layers(x)
        x = x.view(x.size(0), -1)
        x = self.linear_layers(x)
        return x

dropout_parameter.py

# defining the model
model = Net()
# defining the optimizer
optimizer = Adam(model.parameters(), lr=0.0001)
# defining the loss function
criterion = CrossEntropyLoss()
# checking if GPU is available
if torch.cuda.is_available():
    model = model.cuda()
    criterion = criterion.cuda()

print(model)

dropout_train.py

torch.manual_seed(0)

# batch size of the model
batch_size = 128

# number of epochs to train the model
n_epochs = 25

for epoch in range(1, n_epochs+1):

    # keep track of training and validation loss
    train_loss = 0.0
        
    permutation = torch.randperm(train_x.size()[0])

    training_loss = []
    for i in tqdm(range(0,train_x.size()[0], batch_size)):

        indices = permutation[i:i+batch_size]
        batch_x, batch_y = train_x[indices], train_y[indices]
        
        if torch.cuda.is_available():
            batch_x, batch_y = batch_x.cuda(), batch_y.cuda()
        
        optimizer.zero_grad()
        # in case you wanted a semi-full example
        outputs = model(batch_x)
        loss = criterion(outputs,batch_y)

        training_loss.append(loss.item())
        loss.backward()
        optimizer.step()
        
    training_loss = np.average(training_loss)
    print('epoch: \t', epoch, '\t training loss: \t', training_loss)

dropout_train_acc.py

# prediction for training set
prediction = []
target = []
permutation = torch.randperm(train_x.size()[0])
for i in tqdm(range(0,train_x.size()[0], batch_size)):
    indices = permutation[i:i+batch_size]
    batch_x, batch_y = train_x[indices], train_y[indices]

    if torch.cuda.is_available():
        batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    with torch.no_grad():
        output = model(batch_x.cuda())

    softmax = torch.exp(output).cpu()
    prob = list(softmax.numpy())
    predictions = np.argmax(prob, axis=1)
    prediction.append(predictions)
    target.append(batch_y)
    
# training accuracy
accuracy = []
for i in range(len(prediction)):
    accuracy.append(accuracy_score(target[i],prediction[i]))
    
print('training accuracy: \t', np.average(accuracy))

dropout_valid_acc.py

# prediction for validation set
prediction_val = []
target_val = []
permutation = torch.randperm(val_x.size()[0])
for i in tqdm(range(0,val_x.size()[0], batch_size)):
    indices = permutation[i:i+batch_size]
    batch_x, batch_y = val_x[indices], val_y[indices]

    if torch.cuda.is_available():
        batch_x, batch_y = batch_x.cuda(), batch_y.cuda()

    with torch.no_grad():
        output = model(batch_x.cuda())

    softmax = torch.exp(output).cpu()
    prob = list(softmax.numpy())
    predictions = np.argmax(prob, axis=1)
    prediction_val.append(predictions)
    target_val.append(batch_y)
    
# validation accuracy
accuracy_val = []
for i in range(len(prediction_val)):
    accuracy_val.append(accuracy_score(target_val[i],prediction_val[i]))
    
print('validation accuracy: \t', np.average(accuracy_val))

libraries.py

# importing the libraries
import pandas as pd
import numpy as np
from tqdm import tqdm

# for reading and displaying images
from skimage.io import imread
from skimage.transform import resize
import matplotlib.pyplot as plt
%matplotlib inline

# for creating validation set
from sklearn.model_selection import train_test_split

# for evaluating the model
from sklearn.metrics import accuracy_score

# PyTorch libraries and modules
import torch
from torch.autograd import Variable
from torch.nn import Linear, ReLU, CrossEntropyLoss, Sequential, Conv2d, MaxPool2d, Module, Softmax, BatchNorm2d, Dropout
from torch.optim import Adam, SGD

# torchvision for pre-trained models
from torchvision import models

model_combined.py

torch.manual_seed(0)

class Net(Module):   
    def __init__(self):
        super(Net, self).__init__()

        self.cnn_layers = Sequential(
            # Defining a 2D convolution layer
            Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),
            # adding batch normalization
            BatchNorm2d(16),
            MaxPool2d(kernel_size=2, stride=2),
            # adding dropout
            Dropout(),
            # Defining another 2D convolution layer
            Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            ReLU(inplace=True),
            # adding batch normalization
            BatchNorm2d(32),
            MaxPool2d(kernel_size=2, stride=2),
            # adding dropout
            Dropout(),
        )

        self.linear_layers = Sequential(
            Linear(32 * 56 * 56, 2)
        )

    # Defining the forward pass    
    def forward(self, x):
        x = self.cnn_layers(x)
        x = x.view(x.size(0), -1)
        x = self.linear_layers(x)
        return x

parameter.py

# defining the model
model = Net()
# defining the optimizer
optimizer = Adam(model.parameters(), lr=0.0001)
# defining the loss function
criterion = CrossEntropyLoss()
# checking if GPU is available
if torch.cuda.is_available():
    model = model.cuda()
    criterion = criterion.cuda()

print(model)

torch_data.py

# converting training images into torch format
train_x = train_x.reshape(1481, 3, 224, 224)
train_x  = torch.from_numpy(train_x)

# converting the target into torch format
train_y = train_y.astype(int)
train_y = torch.from_numpy(train_y)

# converting validation images into torch format
val_x = val_x.reshape(165, 3, 224, 224)
val_x  = torch.from_numpy(val_x)

# converting the target into torch format
val_y = val_y.astype(int)
val_y = torch.from_numpy(val_y)

validation.py

# defining the target
train_y = train['emergency_or_not'].values

# create validation set
train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size = 0.1, random_state = 13, stratify=train_y)
(train_x.shape, train_y.shape), (val_x.shape, val_y.shape)