cnn_theano.py

from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future

import numpy as np
import theano
import theano.tensor as T
import matplotlib.pyplot as plt

from sklearn.utils import shuffle
from theano.tensor.nnet import conv2d
from theano.tensor.signal.pool import pool_2d

from util import getImageData, error_rate, init_weight_and_bias, init_filter
from ann_theano import HiddenLayer, rmsprop


class ConvPoolLayer(object):
    def __init__(self, mi, mo, fw=5, fh=5, poolsz=(2, 2)):
        # mi = input feature map size
        # mo = output feature map size
        sz = (mo, mi, fw, fh)
        W0 = init_filter(sz, poolsz)
        self.W = theano.shared(W0)
        b0 = np.zeros(mo, dtype=np.float32)
        self.b = theano.shared(b0)
        self.poolsz = poolsz
        self.params = [self.W, self.b]

    def forward(self, X):
        conv_out = conv2d(input=X, filters=self.W)
        pooled_out = pool_2d(
            input=conv_out,
            ws=self.poolsz,
            ignore_border=True,
            mode='max',
        )
        return T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))


class CNN(object):
    def __init__(self, convpool_layer_sizes, hidden_layer_sizes):
        self.convpool_layer_sizes = convpool_layer_sizes
        self.hidden_layer_sizes = hidden_layer_sizes

    def fit(self, X, Y, Xvalid, Yvalid, lr=1e-3, mu=0.99, reg=1e-3, decay=0.99999, eps=1e-10, batch_sz=30, epochs=3, show_fig=True):
        # downcast
        lr = np.float32(lr)
        mu = np.float32(mu)
        reg = np.float32(reg)
        decay = np.float32(decay)
        eps = np.float32(eps)

        X = X.astype(np.float32)
        Xvalid = Xvalid.astype(np.float32)
        Y = Y.astype(np.int32)
        Yvalid = Yvalid.astype(np.int32)

        # initialize convpool layers
        N, c, width, height = X.shape
        mi = c
        outw = width
        outh = height
        self.convpool_layers = []
        for mo, fw, fh in self.convpool_layer_sizes:
            layer = ConvPoolLayer(mi, mo, fw, fh)
            self.convpool_layers.append(layer)
            outw = (outw - fw + 1) // 2
            outh = (outh - fh + 1) // 2
            mi = mo

        # initialize mlp layers
        K = len(set(Y))
        self.hidden_layers = []
        M1 = self.convpool_layer_sizes[-1][0]*outw*outh # size must be same as output of last convpool layer
        count = 0
        for M2 in self.hidden_layer_sizes:
            h = HiddenLayer(M1, M2, count)
            self.hidden_layers.append(h)
            M1 = M2
            count += 1

        # logistic regression layer
        W, b = init_weight_and_bias(M1, K)
        self.W = theano.shared(W, 'W_logreg')
        self.b = theano.shared(b, 'b_logreg')

        # collect params for later use
        self.params = [self.W, self.b]
        for c in self.convpool_layers:
            self.params += c.params
        for h in self.hidden_layers:
            self.params += h.params

        # set up theano functions and variables
        thX = T.tensor4('X', dtype='float32')
        thY = T.ivector('Y')
        pY = self.forward(thX)

        rcost = reg*T.sum([(p*p).sum() for p in self.params])
        cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost
        prediction = self.th_predict(thX)

        cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost, prediction])

        updates = rmsprop(cost, self.params, lr, mu, decay, eps)
        train_op = theano.function(
            inputs=[thX, thY],
            outputs=cost,
            updates=updates
        )

        n_batches = N // batch_sz
        costs = []
        for i in range(epochs):
            X, Y = shuffle(X, Y)
            for j in range(n_batches):
                Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]
                Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]

                train_c = train_op(Xbatch, Ybatch)

                if j % 20 == 0:
                    c, p = cost_predict_op(Xvalid, Yvalid)
                    costs.append(c)
                    e = error_rate(Yvalid, p)
                    print(
                        "i:", i,
                        "j:", j,
                        "nb:", n_batches,
                        "train cost:", train_c,
                        "cost:", c,
                        "error rate:", e
                    )

        if show_fig:
            plt.plot(costs)
            plt.show()

    def forward(self, X):
        Z = X
        for c in self.convpool_layers:
            Z = c.forward(Z)
        Z = Z.flatten(ndim=2)
        for h in self.hidden_layers:
            Z = h.forward(Z)
        return T.nnet.softmax(Z.dot(self.W) + self.b)

    def th_predict(self, X):
        pY = self.forward(X)
        return T.argmax(pY, axis=1)


def main():
    Xtrain, Ytrain, Xvalid, Yvalid = getImageData()
    model = CNN(
        convpool_layer_sizes=[(20, 5, 5), (20, 5, 5)],
        hidden_layer_sizes=[500, 300],
    )
    model.fit(Xtrain, Ytrain, Xvalid, Yvalid)

if __name__ == '__main__':
    main()