"""
This tutorial introduces the LeNet5 neural network architecture using Theano.  LeNet5 is a
convolutional neural network, good for classifying images. This tutorial shows how to build the
architecture, and comes with all the hyper-parameters you need to reproduce the paper's MNIST
results.

The best results are obtained after X iterations of the main program loop, which takes ***
minutes on my workstation (an Intel Core i7, circa July 2009), and *** minutes on my GPU (an
NVIDIA GTX 285 graphics processor).

This implementation simplifies the model in the following ways:

 - LeNetConvPool doesn't implement location-specific gain and bias parameters
 - LeNetConvPool doesn't implement pooling by average, it implements pooling by max.
 - Digit classification is implemented with a logistic regression rather than an RBF network
 - LeNet5 was not fully-connected convolutions at second layer

References:
 - Y. LeCun, L. Bottou, Y. Bengio and P. Haffner: Gradient-Based Learning Applied to Document
   Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998.
   http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
"""

import numpy, theano, cPickle, gzip, time
import theano.tensor as T
import theano.sandbox.softsign
import sys
import pylearn.datasets.MNIST
from pylearn.io import filetensor as ft
from theano.sandbox import conv, downsample

from ift6266 import datasets
import theano,pylearn.version,ift6266

class LeNetConvPoolLayer(object):

    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,2)):
        """
        Allocate a LeNetConvPoolLayer with shared variable internal parameters.
        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights
        :type input: theano.tensor.dtensor4
        :param input: symbolic image tensor, of shape image_shape
        :type filter_shape: tuple or list of length 4
        :param filter_shape: (number of filters, num input feature maps,
                              filter height,filter width)
        :type image_shape: tuple or list of length 4
        :param image_shape: (batch size, num input feature maps,
                             image height, image width)
        :type poolsize: tuple or list of length 2
        :param poolsize: the downsampling (pooling) factor (#rows,#cols)
        """
        assert image_shape[1]==filter_shape[1]
        self.input = input
   
        # initialize weight values: the fan-in of each hidden neuron is
        # restricted by the size of the receptive fields.
        fan_in =  numpy.prod(filter_shape[1:])
        W_values = numpy.asarray( rng.uniform( \
              low = -numpy.sqrt(3./fan_in), \
              high = numpy.sqrt(3./fan_in), \
              size = filter_shape), dtype = theano.config.floatX)
        self.W = theano.shared(value = W_values)

        # the bias is a 1D tensor -- one bias per output feature map
        b_values = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
        self.b = theano.shared(value= b_values)

        # convolve input feature maps with filters
        conv_out = conv.conv2d(input, self.W, 
                filter_shape=filter_shape, image_shape=image_shape)

        # downsample each feature map individually, using maxpooling
        pooled_out = downsample.max_pool2D(conv_out, poolsize, ignore_border=True)

        # add the bias term. Since the bias is a vector (1D array), we first
        # reshape it to a tensor of shape (1,n_filters,1,1). Each bias will thus
        # be broadcasted across mini-batches and feature map width & height
        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))

        # store parameters of this layer
        self.params = [self.W, self.b]


class SigmoidalLayer(object):
    def __init__(self, rng, input, n_in, n_out):
        """
        Typical hidden layer of a MLP: units are fully-connected and have
        sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
        and the bias vector b is of shape (n_out,).
        
        Hidden unit activation is given by: sigmoid(dot(input,W) + b)

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights
        :type input: theano.tensor.dmatrix
        :param input: a symbolic tensor of shape (n_examples, n_in)
        :type n_in: int
        :param n_in: dimensionality of input
        :type n_out: int
        :param n_out: number of hidden units
        """
        self.input = input

        W_values = numpy.asarray( rng.uniform( \
              low = -numpy.sqrt(6./(n_in+n_out)), \
              high = numpy.sqrt(6./(n_in+n_out)), \
              size = (n_in, n_out)), dtype = theano.config.floatX)
        self.W = theano.shared(value = W_values)

        b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
        self.b = theano.shared(value= b_values)

        self.output = T.tanh(T.dot(input, self.W) + self.b)
        self.params = [self.W, self.b]


class LogisticRegression(object):
    """Multi-class Logistic Regression Class

    The logistic regression is fully described by a weight matrix :math:`W` 
    and bias vector :math:`b`. Classification is done by projecting data 
    points onto a set of hyperplanes, the distance to which is used to 
    determine a class membership probability. 
    """

    def __init__(self, input, n_in, n_out):
        """ Initialize the parameters of the logistic regression
        :param input: symbolic variable that describes the input of the 
                      architecture (one minibatch)
        :type n_in: int
        :param n_in: number of input units, the dimension of the space in 
                     which the datapoints lie
        :type n_out: int
        :param n_out: number of output units, the dimension of the space in 
                      which the labels lie
        """ 

        # initialize with 0 the weights W as a matrix of shape (n_in, n_out) 
        self.W = theano.shared( value=numpy.zeros((n_in,n_out),
                                            dtype = theano.config.floatX) )
        # initialize the baises b as a vector of n_out 0s
        self.b = theano.shared( value=numpy.zeros((n_out,), 
                                            dtype = theano.config.floatX) )
        # compute vector of class-membership probabilities in symbolic form
        self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b)
        
        # compute prediction as class whose probability is maximal in 
        # symbolic form
        self.y_pred=T.argmax(self.p_y_given_x, axis=1)

        # list of parameters for this layer
        self.params = [self.W, self.b]

    def negative_log_likelihood(self, y):
        """Return the mean of the negative log-likelihood of the prediction
        of this model under a given target distribution.
        :param y: corresponds to a vector that gives for each example the
                  correct label
        Note: we use the mean instead of the sum so that
        the learning rate is less dependent on the batch size
        """
        return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])

    def errors(self, y):
        """Return a float representing the number of errors in the minibatch 
        over the total number of examples of the minibatch ; zero one
        loss over the size of the minibatch
        """
        # check if y has same dimension of y_pred 
        if y.ndim != self.y_pred.ndim:
            raise TypeError('y should have the same shape as self.y_pred', 
                ('y', target.type, 'y_pred', self.y_pred.type))

        # check if y is of the correct datatype        
        if y.dtype.startswith('int'):
            # the T.neq operator returns a vector of 0s and 1s, where 1
            # represents a mistake in prediction
            return T.mean(T.neq(self.y_pred, y))
        else:
            raise NotImplementedError()


def evaluate_lenet5(learning_rate=0.1, n_iter=200, batch_size=20, n_kern0=20, n_kern1=50, n_layer=3, filter_shape0=5, filter_shape1=5, sigmoide_size=500, dataset='mnist.pkl.gz'):
    rng = numpy.random.RandomState(23455)

    print 'Before load dataset'
    dataset=datasets.nist_digits
    train_batches= dataset.train(batch_size)
    valid_batches=dataset.valid(batch_size)
    test_batches=dataset.test(batch_size)
    #print valid_batches.shape
    #print test_batches.shape
    print 'After load dataset'

    ishape = (32,32)     # this is the size of NIST images
    n_kern2=80
    n_kern3=100
    if n_layer==4:
      filter_shape1=3
      filter_shape2=3
    if n_layer==5:
      filter_shape0=4
      filter_shape1=2
      filter_shape2=2
      filter_shape3=2


    # allocate symbolic variables for the data
    x = T.matrix('x')  # rasterized images
    y = T.lvector()  # the labels are presented as 1D vector of [long int] labels


    ######################
    # BUILD ACTUAL MODEL #
    ######################

    # Reshape matrix of rasterized images of shape (batch_size,28*28)
    # to a 4D tensor, compatible with our LeNetConvPoolLayer
    layer0_input = x.reshape((batch_size,1,32,32))

    # Construct the first convolutional pooling layer:
    # filtering reduces the image size to (32-5+1,32-5+1)=(28,28)
    # maxpooling reduces this further to (28/2,28/2) = (14,14)
    # 4D output tensor is thus of shape (20,20,14,14)
    layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
            image_shape=(batch_size,1,32,32), 
            filter_shape=(n_kern0,1,filter_shape0,filter_shape0), poolsize=(2,2))

    if(n_layer>2):

	# Construct the second convolutional pooling layer
	# filtering reduces the image size to (14-5+1,14-5+1)=(10,10)
	# maxpooling reduces this further to (10/2,10/2) = (5,5)
	# 4D output tensor is thus of shape (20,50,5,5)
	fshape0=(32-filter_shape0+1)/2
	layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
		image_shape=(batch_size,n_kern0,fshape0,fshape0), 
		filter_shape=(n_kern1,n_kern0,filter_shape1,filter_shape1), poolsize=(2,2))

    else:

	fshape0=(32-filter_shape0+1)/2
	layer1_input = layer0.output.flatten(2)
		# construct a fully-connected sigmoidal layer
	layer1 = SigmoidalLayer(rng, input=layer1_input,n_in=n_kern0*fshape0*fshape0, n_out=sigmoide_size)

	layer2 = LogisticRegression(input=layer1.output, n_in=sigmoide_size, n_out=10)
	cost = layer2.negative_log_likelihood(y)
	test_model = theano.function([x,y], layer2.errors(y))
	params = layer2.params+ layer1.params + layer0.params


    if(n_layer>3):

	fshape0=(32-filter_shape0+1)/2
	fshape1=(fshape0-filter_shape1+1)/2
	layer2 = LeNetConvPoolLayer(rng, input=layer1.output,
		image_shape=(batch_size,n_kern1,fshape1,fshape1), 
		filter_shape=(n_kern2,n_kern1,filter_shape2,filter_shape2), poolsize=(2,2))

    if(n_layer>4):


	fshape0=(32-filter_shape0+1)/2
	fshape1=(fshape0-filter_shape1+1)/2
	fshape2=(fshape1-filter_shape2+1)/2
	fshape3=(fshape2-filter_shape3+1)/2
	layer3 = LeNetConvPoolLayer(rng, input=layer2.output,
		image_shape=(batch_size,n_kern2,fshape2,fshape2), 
		filter_shape=(n_kern3,n_kern2,filter_shape3,filter_shape3), poolsize=(2,2))

	layer4_input = layer3.output.flatten(2)

	layer4 = SigmoidalLayer(rng, input=layer4_input, 
					n_in=n_kern3*fshape3*fshape3, n_out=sigmoide_size)

  
	layer5 = LogisticRegression(input=layer4.output, n_in=sigmoide_size, n_out=10)

	cost = layer5.negative_log_likelihood(y)

	test_model = theano.function([x,y], layer5.errors(y))

	params = layer5.params+ layer4.params+ layer3.params+ layer2.params+ layer1.params + layer0.params

    elif(n_layer>3):

	fshape0=(32-filter_shape0+1)/2
	fshape1=(fshape0-filter_shape1+1)/2
	fshape2=(fshape1-filter_shape2+1)/2
	layer3_input = layer2.output.flatten(2)

	layer3 = SigmoidalLayer(rng, input=layer3_input, 
					n_in=n_kern2*fshape2*fshape2, n_out=sigmoide_size)

  
	layer4 = LogisticRegression(input=layer3.output, n_in=sigmoide_size, n_out=10)

	cost = layer4.negative_log_likelihood(y)

	test_model = theano.function([x,y], layer4.errors(y))

	params = layer4.params+ layer3.params+ layer2.params+ layer1.params + layer0.params

 
    elif(n_layer>2):

	fshape0=(32-filter_shape0+1)/2
	fshape1=(fshape0-filter_shape1+1)/2

	# the SigmoidalLayer being fully-connected, it operates on 2D matrices of
	# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
	# This will generate a matrix of shape (20,32*4*4) = (20,512)
	layer2_input = layer1.output.flatten(2)

	# construct a fully-connected sigmoidal layer
	layer2 = SigmoidalLayer(rng, input=layer2_input, 
					n_in=n_kern1*fshape1*fshape1, n_out=sigmoide_size)

  
	# classify the values of the fully-connected sigmoidal layer
	layer3 = LogisticRegression(input=layer2.output, n_in=sigmoide_size, n_out=10)

	# the cost we minimize during training is the NLL of the model
	cost = layer3.negative_log_likelihood(y)

	# create a function to compute the mistakes that are made by the model
	test_model = theano.function([x,y], layer3.errors(y))

	# create a list of all model parameters to be fit by gradient descent
	params = layer3.params+ layer2.params+ layer1.params + layer0.params
    	
      
  
		
    
    # create a list of gradients for all model parameters
    grads = T.grad(cost, params)

    # train_model is a function that updates the model parameters by SGD
    # Since this model has many parameters, it would be tedious to manually
    # create an update rule for each model parameter. We thus create the updates
    # dictionary by automatically looping over all (params[i],grads[i])  pairs.
    updates = {}
    for param_i, grad_i in zip(params, grads):
        updates[param_i] = param_i - learning_rate * grad_i
    train_model = theano.function([x, y], cost, updates=updates)


    ###############
    # TRAIN MODEL #
    ###############

    #n_minibatches        = len(train_batches) 
    n_minibatches=0
    n_valid=0
    n_test=0
    for x, y in dataset.train(batch_size):
	if x.shape[0] == batch_size:
	    n_minibatches+=1
    n_minibatches*=batch_size
    print n_minibatches

    for x, y in dataset.valid(batch_size):
	if x.shape[0] == batch_size:
	    n_valid+=1
    n_valid*=batch_size
    print n_valid

    for x, y in dataset.test(batch_size):
	if x.shape[0] == batch_size:
	    n_test+=1
    n_test*=batch_size
    print n_test
  

    # early-stopping parameters
    patience              = 10000 # look as this many examples regardless
    patience_increase     = 2     # wait this much longer when a new best is 
                                  # found
    improvement_threshold = 0.995 # a relative improvement of this much is 
                                  # considered significant
    validation_frequency  = n_minibatches  # go through this many 
                                  # minibatche before checking the network 
                                  # on the validation set; in this case we 
                                  # check every epoch 

    best_params          = None
    best_validation_loss = float('inf')
    best_iter            = 0
    test_score           = 0.
    start_time = time.clock()


    # have a maximum of `n_iter` iterations through the entire dataset
    iter=0
    for epoch in xrange(n_iter):
	for x, y in train_batches:
	    if x.shape[0] != batch_size:
		continue
	    iter+=1

	    # get epoch and minibatch index
	    #epoch           = iter / n_minibatches
	    minibatch_index =  iter % n_minibatches
	    
	    if iter %100 == 0:
		print 'training @ iter = ', iter
	    cost_ij = train_model(x,y)


	# compute zero-one loss on validation set 
	this_validation_loss = 0.
	for x,y in valid_batches:
	    if x.shape[0] != batch_size:
		continue
	    # sum up the errors for each minibatch
	    this_validation_loss += test_model(x,y)

	# get the average by dividing with the number of minibatches
	this_validation_loss /= n_valid
	print('epoch %i, minibatch %i/%i, validation error %f %%' % \
	      (epoch, minibatch_index+1, n_minibatches, \
		this_validation_loss*100.))


	# if we got the best validation score until now
	if this_validation_loss < best_validation_loss:

	    #improve patience if loss improvement is good enough
	    if this_validation_loss < best_validation_loss *  \
		  improvement_threshold :
		patience = max(patience, iter * patience_increase)

	    # save best validation score and iteration number
	    best_validation_loss = this_validation_loss
	    best_iter = iter

	    # test it on the test set
	    test_score = 0.
	    for x,y in test_batches:
		if x.shape[0] != batch_size:
		    continue
		test_score += test_model(x,y)
	    test_score /= n_test
	    print(('     epoch %i, minibatch %i/%i, test error of best '
		  'model %f %%') % 
			(epoch, minibatch_index+1, n_minibatches,
			  test_score*100.))

	if patience <= iter :
	    break

    end_time = time.clock()
    print('Optimization complete.')
    print('Best validation score of %f %% obtained at iteration %i,'\
          'with test performance %f %%' %  
          (best_validation_loss * 100., best_iter, test_score*100.))
    print('The code ran for %f minutes' % ((end_time-start_time)/60.))

    return (best_validation_loss * 100., test_score*100., (end_time-start_time)/60., best_iter)

if __name__ == '__main__':
    evaluate_lenet5()

def experiment(state, channel):
    print 'start experiment'
    (best_validation_loss, test_score, minutes_trained, iter) = evaluate_lenet5(state.learning_rate, state.n_iter, state.batch_size, state.n_kern0, state.n_kern1, state.n_layer, state.filter_shape0, state.filter_shape1,state.sigmoide_size)
    print 'end experiment'

    pylearn.version.record_versions(state,[theano,ift6266,pylearn])
    
    state.best_validation_loss = best_validation_loss
    state.test_score = test_score
    state.minutes_trained = minutes_trained
    state.iter = iter

    return channel.COMPLETE