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

# # Deep Convolutional Neural Networks (AlexNet)

# In[1]:


import d2l
from mxnet import gluon, np, npx
from mxnet.gluon import nn
npx.set_np()

train_iter, test_iter = d2l.load_data_fashion_mnist(
    batch_size=128, resize=224)


# The model

# In[2]:


net = nn.Sequential()
net.add(# A larger 11 x 11 window to capture large objects.
        nn.Conv2D(96, kernel_size=11, strides=4, activation='relu'),
        nn.MaxPool2D(pool_size=3, strides=2),
        # Make the convolution window smaller, and increase the
        # number of output channels
        nn.Conv2D(256, kernel_size=5, padding=2, activation='relu'),
        nn.MaxPool2D(pool_size=3, strides=2),
        # Use three successive convolutional layers and a smaller convolution
        # window. 
        nn.Conv2D(384, kernel_size=3, padding=1, activation='relu'),
        nn.Conv2D(384, kernel_size=3, padding=1, activation='relu'),
        nn.Conv2D(256, kernel_size=3, padding=1, activation='relu'),
        nn.MaxPool2D(pool_size=3, strides=2),
        # Several times larger fully connected layers with dropout
        nn.Dense(4096, activation="relu"), nn.Dropout(0.5),
        nn.Dense(4096, activation="relu"), nn.Dropout(0.5),
        nn.Dense(10))


# Check the outputs layer by layer

# In[3]:


X = np.random.uniform(size=(1, 1, 224, 224))
net.initialize()
for layer in net:
    X = layer(X)
    print(layer.name, 'output shape:\t', X.shape)


# Training

# In[4]:


d2l.train_ch5(net, train_iter, test_iter, num_epochs=10, lr=0.01)