
# coding: utf-8

# *Python Machine Learning 2nd Edition* by [Sebastian Raschka](https://sebastianraschka.com), Packt Publishing Ltd. 2017
# 
# Code Repository: https://github.com/rasbt/python-machine-learning-book-2nd-edition
# 
# Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-2nd-edition/blob/master/LICENSE.txt)

# # Python Machine Learning - Code Examples

# # Chapter 12 - Implementing a Multi-layer Artificial Neural Network from Scratch
# 

# Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).

# In[1]:




# *The use of `watermark` is optional. You can install this IPython extension via "`pip install watermark`". For more information, please see: https://github.com/rasbt/watermark.*

# ### Overview

# - [Modeling complex functions with artificial neural networks](#Modeling-complex-functions-with-artificial-neural-networks)
#   - [Single-layer neural network recap](#Single-layer-neural-network-recap)
#   - [Introducing the multi-layer neural network architecture](#Introducing-the-multi-layer-neural-network-architecture)
#   - [Activating a neural network via forward propagation](#Activating-a-neural-network-via-forward-propagation)
# - [Classifying handwritten digits](#Classifying-handwritten-digits)
#   - [Obtaining the MNIST dataset](#Obtaining-the-MNIST-dataset)
#   - [Implementing a multi-layer perceptron](#Implementing-a-multi-layer-perceptron)
# - [Training an artificial neural network](#Training-an-artificial-neural-network)
#   - [Computing the logistic cost function](#Computing-the-logistic-cost-function)
#   - [Developing your intuition for backpropagation](#Developing-your-intuition-for-backpropagation)
#   - [Training neural networks via backpropagation](#Training-neural-networks-via-backpropagation)
# - [Convergence in neural networks](#Convergence-in-neural-networks)
# - [Summary](#Summary)


# In[2]:


from IPython.display import Image


# # Modeling complex functions with artificial neural networks

# ...

# ## Single-layer neural network recap

# In[3]:





# ## Introducing the multi-layer neural network architecture

# In[4]:




# In[5]:





# ## Activating a neural network via forward propagation

# In[6]:





# # Classifying handwritten digits

# ...

# ## Obtaining the MNIST dataset

# The MNIST dataset is publicly available at http://yann.lecun.com/exdb/mnist/ and consists of the following four parts:
# 
# - Training set images: train-images-idx3-ubyte.gz (9.9 MB, 47 MB unzipped, 60,000 samples)
# - Training set labels: train-labels-idx1-ubyte.gz (29 KB, 60 KB unzipped, 60,000 labels)
# - Test set images: t10k-images-idx3-ubyte.gz (1.6 MB, 7.8 MB, 10,000 samples)
# - Test set labels: t10k-labels-idx1-ubyte.gz (5 KB, 10 KB unzipped, 10,000 labels)
# 
# In this section, we will only be working with a subset of MNIST, thus, we only need to download the training set images and training set labels. After downloading the files, I recommend unzipping the files using the Unix/Linux gzip tool from the terminal for efficiency, e.g., using the command 
# 
#     gzip *ubyte.gz -d
#  
# in your local MNIST download directory, or, using your favorite unzipping tool if you are working with a machine running on Microsoft Windows. The images are stored in byte form, and using the following function, we will read them into NumPy arrays that we will use to train our MLP.
# 

# In[7]:


import os
import struct
import numpy as np
 
def load_mnist(path, kind='train'):
    """Load MNIST data from `path`"""
    labels_path = os.path.join(path, 
                               '%s-labels-idx1-ubyte' % kind)
    images_path = os.path.join(path, 
                               '%s-images-idx3-ubyte' % kind)
        
    with open(labels_path, 'rb') as lbpath:
        magic, n = struct.unpack('>II', 
                                 lbpath.read(8))
        labels = np.fromfile(lbpath, 
                             dtype=np.uint8)

    with open(images_path, 'rb') as imgpath:
        magic, num, rows, cols = struct.unpack(">IIII", 
                                               imgpath.read(16))
        images = np.fromfile(imgpath, 
                             dtype=np.uint8).reshape(len(labels), 784)
        images = ((images / 255.) - .5) * 2
 
    return images, labels


# In[8]:




# In[9]:


# unzips mnist

import sys
import gzip
import shutil

if (sys.version_info > (3, 0)):
    writemode = 'wb'
else:
    writemode = 'w'

zipped_mnist = [f for f in os.listdir('./') if f.endswith('ubyte.gz')]
for z in zipped_mnist:
    with gzip.GzipFile(z, mode='rb') as decompressed, open(z[:-3], writemode) as outfile:
        outfile.write(decompressed.read()) 


# In[10]:


X_train, y_train = load_mnist('', kind='train')
print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))


# In[11]:


X_test, y_test = load_mnist('', kind='t10k')
print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))


# Visualize the first digit of each class:

# In[12]:


import matplotlib.pyplot as plt

fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,)
ax = ax.flatten()
for i in range(10):
    img = X_train[y_train == i][0].reshape(28, 28)
    ax[i].imshow(img, cmap='Greys')

ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
# plt.savefig('images/12_5.png', dpi=300)
plt.show()


# Visualize 25 different versions of "7":

# In[13]:


fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)
ax = ax.flatten()
for i in range(25):
    img = X_train[y_train == 7][i].reshape(28, 28)
    ax[i].imshow(img, cmap='Greys')

ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
# plt.savefig('images/12_6.png', dpi=300)
plt.show()


# In[14]:


import numpy as np

np.savez_compressed('mnist_scaled.npz', 
                    X_train=X_train,
                    y_train=y_train,
                    X_test=X_test,
                    y_test=y_test)


# In[15]:


mnist = np.load('mnist_scaled.npz')
mnist.files


# In[16]:


X_train, y_train, X_test, y_test = [mnist[f] for f in ['X_train', 'y_train', 
                                    'X_test', 'y_test']]

del mnist

X_train.shape



# ## Implementing a multi-layer perceptron

# In[17]:


import numpy as np
import sys


class NeuralNetMLP(object):
    """ Feedforward neural network / Multi-layer perceptron classifier.

    Parameters
    ------------
    n_hidden : int (default: 30)
        Number of hidden units.
    l2 : float (default: 0.)
        Lambda value for L2-regularization.
        No regularization if l2=0. (default)
    epochs : int (default: 100)
        Number of passes over the training set.
    eta : float (default: 0.001)
        Learning rate.
    shuffle : bool (default: True)
        Shuffles training data every epoch if True to prevent circles.
    minibatche_size : int (default: 1)
        Number of training samples per minibatch.
    seed : int (default: None)
        Random seed for initalizing weights and shuffling.

    Attributes
    -----------
    eval_ : dict
      Dictionary collecting the cost, training accuracy,
      and validation accuracy for each epoch during training.

    """
    def __init__(self, n_hidden=30,
                 l2=0., epochs=100, eta=0.001,
                 shuffle=True, minibatch_size=1, seed=None):

        self.random = np.random.RandomState(seed)
        self.n_hidden = n_hidden
        self.l2 = l2
        self.epochs = epochs
        self.eta = eta
        self.shuffle = shuffle
        self.minibatch_size = minibatch_size

    def _onehot(self, y, n_classes):
        """Encode labels into one-hot representation

        Parameters
        ------------
        y : array, shape = [n_samples]
            Target values.

        Returns
        -----------
        onehot : array, shape = (n_samples, n_labels)

        """
        onehot = np.zeros((n_classes, y.shape[0]))
        for idx, val in enumerate(y.astype(int)):
            onehot[val, idx] = 1.
        return onehot.T

    def _sigmoid(self, z):
        """Compute logistic function (sigmoid)"""
        return 1. / (1. + np.exp(-np.clip(z, -250, 250)))

    def _forward(self, X):
        """Compute forward propagation step"""

        # step 1: net input of hidden layer
        # [n_samples, n_features] dot [n_features, n_hidden]
        # -> [n_samples, n_hidden]
        z_h = np.dot(X, self.w_h) + self.b_h

        # step 2: activation of hidden layer
        a_h = self._sigmoid(z_h)

        # step 3: net input of output layer
        # [n_samples, n_hidden] dot [n_hidden, n_classlabels]
        # -> [n_samples, n_classlabels]

        z_out = np.dot(a_h, self.w_out) + self.b_out

        # step 4: activation output layer
        a_out = self._sigmoid(z_out)

        return z_h, a_h, z_out, a_out

    def _compute_cost(self, y_enc, output):
        """Compute cost function.

        Parameters
        ----------
        y_enc : array, shape = (n_samples, n_labels)
            one-hot encoded class labels.
        output : array, shape = [n_samples, n_output_units]
            Activation of the output layer (forward propagation)

        Returns
        ---------
        cost : float
            Regularized cost

        """
        L2_term = (self.l2 *
                   (np.sum(self.w_h ** 2.) +
                    np.sum(self.w_out ** 2.)))

        term1 = -y_enc * (np.log(output))
        term2 = (1. - y_enc) * np.log(1. - output)
        cost = np.sum(term1 - term2) + L2_term
        return cost

    def predict(self, X):
        """Predict class labels

        Parameters
        -----------
        X : array, shape = [n_samples, n_features]
            Input layer with original features.

        Returns:
        ----------
        y_pred : array, shape = [n_samples]
            Predicted class labels.

        """
        z_h, a_h, z_out, a_out = self._forward(X)
        y_pred = np.argmax(z_out, axis=1)
        return y_pred

    def fit(self, X_train, y_train, X_valid, y_valid):
        """ Learn weights from training data.

        Parameters
        -----------
        X_train : array, shape = [n_samples, n_features]
            Input layer with original features.
        y_train : array, shape = [n_samples]
            Target class labels.
        X_valid : array, shape = [n_samples, n_features]
            Sample features for validation during training
        y_valid : array, shape = [n_samples]
            Sample labels for validation during training

        Returns:
        ----------
        self

        """
        n_output = np.unique(y_train).shape[0]  # number of class labels
        n_features = X_train.shape[1]

        ########################
        # Weight initialization
        ########################

        # weights for input -> hidden
        self.b_h = np.zeros(self.n_hidden)
        self.w_h = self.random.normal(loc=0.0, scale=0.1,
                                      size=(n_features, self.n_hidden))

        # weights for hidden -> output
        self.b_out = np.zeros(n_output)
        self.w_out = self.random.normal(loc=0.0, scale=0.1,
                                        size=(self.n_hidden, n_output))

        epoch_strlen = len(str(self.epochs))  # for progress formatting
        self.eval_ = {'cost': [], 'train_acc': [], 'valid_acc': []}

        y_train_enc = self._onehot(y_train, n_output)

        # iterate over training epochs
        for i in range(self.epochs):

            # iterate over minibatches
            indices = np.arange(X_train.shape[0])

            if self.shuffle:
                self.random.shuffle(indices)

            for start_idx in range(0, indices.shape[0] - self.minibatch_size +
                                   1, self.minibatch_size):
                batch_idx = indices[start_idx:start_idx + self.minibatch_size]

                # forward propagation
                z_h, a_h, z_out, a_out = self._forward(X_train[batch_idx])

                ##################
                # Backpropagation
                ##################

                # [n_samples, n_classlabels]
                sigma_out = a_out - y_train_enc[batch_idx]

                # [n_samples, n_hidden]
                sigmoid_derivative_h = a_h * (1. - a_h)

                # [n_samples, n_classlabels] dot [n_classlabels, n_hidden]
                # -> [n_samples, n_hidden]
                sigma_h = (np.dot(sigma_out, self.w_out.T) *
                           sigmoid_derivative_h)

                # [n_features, n_samples] dot [n_samples, n_hidden]
                # -> [n_features, n_hidden]
                grad_w_h = np.dot(X_train[batch_idx].T, sigma_h)
                grad_b_h = np.sum(sigma_h, axis=0)

                # [n_hidden, n_samples] dot [n_samples, n_classlabels]
                # -> [n_hidden, n_classlabels]
                grad_w_out = np.dot(a_h.T, sigma_out)
                grad_b_out = np.sum(sigma_out, axis=0)

                # Regularization and weight updates
                delta_w_h = (grad_w_h + self.l2*self.w_h)
                delta_b_h = grad_b_h # bias is not regularized
                self.w_h -= self.eta * delta_w_h
                self.b_h -= self.eta * delta_b_h

                delta_w_out = (grad_w_out + self.l2*self.w_out)
                delta_b_out = grad_b_out  # bias is not regularized
                self.w_out -= self.eta * delta_w_out
                self.b_out -= self.eta * delta_b_out

            #############
            # Evaluation
            #############

            # Evaluation after each epoch during training
            z_h, a_h, z_out, a_out = self._forward(X_train)
            
            cost = self._compute_cost(y_enc=y_train_enc,
                                      output=a_out)

            y_train_pred = self.predict(X_train)
            y_valid_pred = self.predict(X_valid)

            train_acc = ((np.sum(y_train == y_train_pred)).astype(np.float) /
                         X_train.shape[0])
            valid_acc = ((np.sum(y_valid == y_valid_pred)).astype(np.float) /
                         X_valid.shape[0])

            sys.stderr.write('\r%0*d/%d | Cost: %.2f '
                             '| Train/Valid Acc.: %.2f%%/%.2f%% ' %
                             (epoch_strlen, i+1, self.epochs, cost,
                              train_acc*100, valid_acc*100))
            sys.stderr.flush()

            self.eval_['cost'].append(cost)
            self.eval_['train_acc'].append(train_acc)
            self.eval_['valid_acc'].append(valid_acc)

        return self


# In[18]:


n_epochs = 200

## @Readers: PLEASE IGNORE IF-STATEMENT BELOW
##
## This cell is meant to run fewer epochs when
## the notebook is run on the Travis Continuous Integration
## platform to test the code on a smaller dataset
## to prevent timeout errors; it just serves a debugging tool

if 'TRAVIS' in os.environ:
    n_epochs = 20


# In[19]:


nn = NeuralNetMLP(n_hidden=100, 
                  l2=0.01, 
                  epochs=n_epochs, 
                  eta=0.0005,
                  minibatch_size=100, 
                  shuffle=True,
                  seed=1)

nn.fit(X_train=X_train[:55000], 
       y_train=y_train[:55000],
       X_valid=X_train[55000:],
       y_valid=y_train[55000:])


# ---
# **Note**
# 
# In the fit method of the MLP example above,
# 
# ```python
# 
# for idx in mini:
# ...
#     # compute gradient via backpropagation
#     grad1, grad2 = self._get_gradient(a1=a1, a2=a2,
#                                       a3=a3, z2=z2,
#                                       y_enc=y_enc[:, idx],
#                                       w1=self.w1,
#                                       w2=self.w2)
# 
#     delta_w1, delta_w2 = self.eta * grad1, self.eta * grad2
#     self.w1 -= (delta_w1 + (self.alpha * delta_w1_prev))
#     self.w2 -= (delta_w2 + (self.alpha * delta_w2_prev))
#     delta_w1_prev, delta_w2_prev = delta_w1, delta_w2
# ```
# 
# `delta_w1_prev` (same applies to `delta_w2_prev`) is a memory view on `delta_w1` via  
# 
# ```python
# delta_w1_prev = delta_w1
# ```
# on the last line. This could be problematic, since updating `delta_w1 = self.eta * grad1` would change `delta_w1_prev` as well when we iterate over the for loop. Note that this is not the case here, because we assign a new array to `delta_w1` in each iteration -- the gradient array times the learning rate:
# 
# ```python
# delta_w1 = self.eta * grad1
# ```
# 
# The assignment shown above leaves the `delta_w1_prev` pointing to the "old" `delta_w1` array. To illustrates this with a simple snippet, consider the following example:
# 
# 

# In[20]:


import numpy as np

a = np.arange(5)
b = a
print('a & b', np.may_share_memory(a, b))


a = np.arange(5)
print('a & b', np.may_share_memory(a, b))


# (End of note.)
# 
# ---

# In[21]:


import matplotlib.pyplot as plt

plt.plot(range(nn.epochs), nn.eval_['cost'])
plt.ylabel('Cost')
plt.xlabel('Epochs')
#plt.savefig('images/12_07.png', dpi=300)
plt.show()


# In[22]:


plt.plot(range(nn.epochs), nn.eval_['train_acc'], 
         label='training')
plt.plot(range(nn.epochs), nn.eval_['valid_acc'], 
         label='validation', linestyle='--')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend()
#plt.savefig('images/12_08.png', dpi=300)
plt.show()


# In[23]:


y_test_pred = nn.predict(X_test)
acc = (np.sum(y_test == y_test_pred)
       .astype(np.float) / X_test.shape[0])

print('Test accuracy: %.2f%%' % (acc * 100))


# In[24]:


miscl_img = X_test[y_test != y_test_pred][:25]
correct_lab = y_test[y_test != y_test_pred][:25]
miscl_lab = y_test_pred[y_test != y_test_pred][:25]

fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)
ax = ax.flatten()
for i in range(25):
    img = miscl_img[i].reshape(28, 28)
    ax[i].imshow(img, cmap='Greys', interpolation='nearest')
    ax[i].set_title('%d) t: %d p: %d' % (i+1, correct_lab[i], miscl_lab[i]))

ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
#plt.savefig('images/12_09.png', dpi=300)
plt.show()



# # Training an artificial neural network

# ...

# ## Computing the logistic cost function

# In[25]:





# ## Developing your intuition for backpropagation

# ...

# ## Training neural networks via backpropagation

# In[26]:




# In[6]:





# # Convergence in neural networks

# In[28]:





# ...

# # Summary

# ...
