#!/usr/bin/env python
# coding: utf-8

# Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
# - Author: Sebastian Raschka
# - GitHub Repository: https://github.com/rasbt/deeplearning-models

# In[1]:


get_ipython().run_line_magic('load_ext', 'watermark')
get_ipython().run_line_magic('watermark', "-a 'Sebastian Raschka' -v -p torch")


# - Runs on CPU or GPU (if available)

# # Model Zoo -- Convolutional Neural Network

# ## Imports

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import time
import numpy as np
import torch
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader


if torch.cuda.is_available():
    torch.backends.cudnn.deterministic = True


# ## Settings and Dataset

# In[3]:


##########################
### SETTINGS
##########################

# Device
device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")

# Hyperparameters
random_seed = 1
learning_rate = 0.05
num_epochs = 10
batch_size = 128

# Architecture
num_classes = 10


##########################
### MNIST DATASET
##########################

# Note transforms.ToTensor() scales input images
# to 0-1 range
train_dataset = datasets.MNIST(root='data', 
                               train=True, 
                               transform=transforms.ToTensor(),
                               download=True)

test_dataset = datasets.MNIST(root='data', 
                              train=False, 
                              transform=transforms.ToTensor())


train_loader = DataLoader(dataset=train_dataset, 
                          batch_size=batch_size, 
                          shuffle=True)

test_loader = DataLoader(dataset=test_dataset, 
                         batch_size=batch_size, 
                         shuffle=False)

# Checking the dataset
for images, labels in train_loader:  
    print('Image batch dimensions:', images.shape)
    print('Image label dimensions:', labels.shape)
    break


# ## Model

# In[4]:


##########################
### MODEL
##########################


class ConvNet(torch.nn.Module):

    def __init__(self, num_classes):
        super(ConvNet, self).__init__()
        
        # calculate same padding:
        # (w - k + 2*p)/s + 1 = o
        # => p = (s(o-1) - w + k)/2
        
        # 28x28x1 => 28x28x8
        self.conv_1 = torch.nn.Conv2d(in_channels=1,
                                      out_channels=8,
                                      kernel_size=(3, 3),
                                      stride=(1, 1),
                                      padding=1) # (1(28-1) - 28 + 3) / 2 = 1
        # 28x28x8 => 14x14x8
        self.pool_1 = torch.nn.MaxPool2d(kernel_size=(2, 2),
                                         stride=(2, 2),
                                         padding=0) # (2(14-1) - 28 + 2) = 0                                       
        # 14x14x8 => 14x14x16
        self.conv_2 = torch.nn.Conv2d(in_channels=8,
                                      out_channels=16,
                                      kernel_size=(3, 3),
                                      stride=(1, 1),
                                      padding=1) # (1(14-1) - 14 + 3) / 2 = 1                 
        # 14x14x16 => 7x7x16                             
        self.pool_2 = torch.nn.MaxPool2d(kernel_size=(2, 2),
                                         stride=(2, 2),
                                         padding=0) # (2(7-1) - 14 + 2) = 0

        self.linear_1 = torch.nn.Linear(7*7*16, num_classes)

        # optionally initialize weights from Gaussian;
        # Guassian weight init is not recommended and only for demonstration purposes
        for m in self.modules():
            if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Linear):
                m.weight.data.normal_(0.0, 0.01)
                m.bias.data.zero_()
                if m.bias is not None:
                    m.bias.detach().zero_()
        
        
    def forward(self, x):
        out = self.conv_1(x)
        out = F.relu(out)
        out = self.pool_1(out)

        out = self.conv_2(out)
        out = F.relu(out)
        out = self.pool_2(out)
        
        logits = self.linear_1(out.view(-1, 7*7*16))
        probas = F.softmax(logits, dim=1)
        return logits, probas

    
torch.manual_seed(random_seed)
model = ConvNet(num_classes=num_classes)

model = model.to(device)

optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  


# ## Training

# In[5]:


def compute_accuracy(model, data_loader):
    correct_pred, num_examples = 0, 0
    for features, targets in data_loader:
        features = features.to(device)
        targets = targets.to(device)
        logits, probas = model(features)
        _, predicted_labels = torch.max(probas, 1)
        num_examples += targets.size(0)
        correct_pred += (predicted_labels == targets).sum()
    return correct_pred.float()/num_examples * 100
    

start_time = time.time()    
for epoch in range(num_epochs):
    model = model.train()
    for batch_idx, (features, targets) in enumerate(train_loader):
        
        features = features.to(device)
        targets = targets.to(device)

        ### FORWARD AND BACK PROP
        logits, probas = model(features)
        cost = F.cross_entropy(logits, targets)
        optimizer.zero_grad()
        
        cost.backward()
        
        ### UPDATE MODEL PARAMETERS
        optimizer.step()
        
        ### LOGGING
        if not batch_idx % 50:
            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' 
                   %(epoch+1, num_epochs, batch_idx, 
                     len(train_loader), cost))
    
    model = model.eval()
    print('Epoch: %03d/%03d training accuracy: %.2f%%' % (
          epoch+1, num_epochs, 
          compute_accuracy(model, train_loader)))

    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))
    
print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))


# ## Evaluation

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with torch.set_grad_enabled(False): # save memory during inference
    print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))


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get_ipython().run_line_magic('watermark', '-iv')