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Theano 实例：卷积神经网络

In [1]:

import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
import numpy as np
from load import mnist

srng = RandomStreams()

Using gpu device 1: Tesla C2075 (CNMeM is disabled)

从前一节导入有用的函数：

In [2]:

def floatX(X):
    return np.asarray(X, dtype=theano.config.floatX)

def init_weights(shape):
    return theano.shared(floatX(np.random.randn(*shape) * 0.01))

def rectify(X):
    return T.maximum(X, 0.)

def softmax(X):
    e_x = T.exp(X - X.max(axis=1).dimshuffle(0, 'x'))
    return e_x / e_x.sum(axis=1).dimshuffle(0, 'x')

def dropout(X, p=0.):
    if p > 0:
        retain_prob = 1 - p
        X *= srng.binomial(X.shape, p=retain_prob, dtype=theano.config.floatX)
        X /= retain_prob
    return X

def RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):
    grads = T.grad(cost=cost, wrt=params)
    updates = []
    for p, g in zip(params, grads):
        acc = theano.shared(p.get_value() * 0.)
        acc_new = rho * acc + (1 - rho) * g ** 2
        gradient_scaling = T.sqrt(acc_new + epsilon)
        g = g / gradient_scaling
        updates.append((acc, acc_new))
        updates.append((p, p - lr * g))
    return updates

与前一节不同，我们使用卷积神经网络来实现这次的模型，为此，我们需要导入 2 维的卷积和池化函数：

In [3]:

from theano.tensor.nnet.conv import conv2d
from theano.tensor.signal.downsample import max_pool_2d

conv2d 函数接受两个输入：

  • 对应输入的 4D 张量，其形状如下：

    [mini-batch size, number of feature maps at layer m-1, image height, image width]

  • 对应参数矩阵的 4D 张量，其形状如下：

    [number of feature maps at layer m, number of feature maps at layer m-1, filter height, filter width]

为了对图像使用卷积，我们需要将图像转化为原始的 28 × 28 大小，同时添加一维表示图像的通道数（黑白图像为 1）：

In [4]:

trX, teX, trY, teY = mnist(onehot=True)

trX = trX.reshape(-1, 1, 28, 28)
teX = teX.reshape(-1, 1, 28, 28)

注意，对于 reshape 方法，传入的参数是 -1 表示该维的维度将根据其他参数自动计算。

模型首先进行三层卷积加池化操作，然后在第三层的输出中加一个全连结层，最后在第四层加上一个 softmax 层：

In [5]:

def model(X, w, w2, w3, w4, p_drop_conv, p_drop_hidden):

    # X:  128 * 1 * 28 * 28
    # w:  32 * 1 * 3 * 3
    # full mode
    # l1a: 128 * 32 * (28 + 3 - 1) * (28 + 3 - 1)
    l1a = rectify(conv2d(X, w, border_mode='full'))
    # l1a: 128 * 32 * 30 * 30
    # ignore_border False
    # l1:  128 * 32 * (30 / 2) * (30 / 2)
    l1 = max_pool_2d(l1a, (2, 2), ignore_border=False)
    l1 = dropout(l1, p_drop_conv)

    # l1:  128 * 32 * 15 * 15
    # w2:  64 * 32 * 3 * 3
    # valid mode
    # l2a: 128 * 64 * (15 - 3 + 1) * (15 - 3 + 1)
    l2a = rectify(conv2d(l1, w2))    
    # l2a: 128 * 64 * 13 * 13
    # l2:  128 * 64 * (13 / 2 + 1) * (13 / 2 + 1)
    l2 = max_pool_2d(l2a, (2, 2), ignore_border=False)
    l2 = dropout(l2, p_drop_conv)

    # l2:  128 * 64 * 7 * 7
    # w3:  128 * 64 * 3 * 3
    # l3a: 128 * 128 * (7 - 3 + 1) * (7 - 3 + 1)
    l3a = rectify(conv2d(l2, w3))
    # l3a: 128 * 128 * 5 * 5
    # l3b: 128 * 128 * (5 / 2 + 1) * (5 / 2 + 1)
    l3b = max_pool_2d(l3a, (2, 2), ignore_border=False)    
    # l3b: 128 * 128 * 3 * 3
    # l3:  128 * (128 * 3 * 3)
    l3 = T.flatten(l3b, outdim=2)
    l3 = dropout(l3, p_drop_conv)

    # l3: 128 * (128 * 3 * 3)
    # w4: (128 * 3 * 3) * 625
    # l4: 128 * 625
    l4 = rectify(T.dot(l3, w4))
    l4 = dropout(l4, p_drop_hidden)

    # l5:  128 * 625
    # w5:  625 * 10
    # pyx: 128 * 10
    pyx = softmax(T.dot(l4, w_o))
    return l1, l2, l3, l4, pyx

定义符号变量：

In [6]:

X = T.ftensor4()
Y = T.fmatrix()

w = init_weights((32, 1, 3, 3))
w2 = init_weights((64, 32, 3, 3))
w3 = init_weights((128, 64, 3, 3))
w4 = init_weights((128 * 3 * 3, 625))
w_o = init_weights((625, 10))

使用带 dropout 的模型进行训练：

In [7]:

noise_l1, noise_l2, noise_l3, noise_l4, noise_py_x = model(X, w, w2, w3, w4, 0.2, 0.5)

使用不带 dropout 的模型进行预测：

In [8]:

l1, l2, l3, l4, py_x = model(X, w, w2, w3, w4, 0., 0.)
y_x = T.argmax(py_x, axis=1)

定义损失函数和迭代规则：

In [9]:

cost = T.mean(T.nnet.categorical_crossentropy(noise_py_x, Y))
params = [w, w2, w3, w4, w_o]
updates = RMSprop(cost, params, lr=0.001)

开始训练：

In [10]:

train = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True)
predict = theano.function(inputs=[X], outputs=y_x, allow_input_downcast=True)

for i in range(50):
    for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):
        cost = train(trX[start:end], trY[start:end])
    print "iter {:03d}, {:.3f}".format(i + 1, np.mean(np.argmax(teY, axis=1) == predict(teX)))

iter 001, 0.917
iter 002, 0.974
iter 003, 0.983
iter 004, 0.984
iter 005, 0.987
iter 006, 0.989
iter 007, 0.991
iter 008, 0.993
iter 009, 0.991
iter 010, 0.992
iter 011, 0.993
iter 012, 0.992
iter 013, 0.992
iter 014, 0.992
iter 015, 0.993
iter 016, 0.992
iter 017, 0.994
iter 018, 0.993
iter 019, 0.993
iter 020, 0.994
iter 021, 0.993
iter 022, 0.993
iter 023, 0.993
iter 024, 0.992
iter 025, 0.994
iter 026, 0.993
iter 027, 0.994
iter 028, 0.993
iter 029, 0.993
iter 030, 0.994
iter 031, 0.994
iter 032, 0.993
iter 033, 0.994
iter 034, 0.994
iter 035, 0.994
iter 036, 0.994
iter 037, 0.994
iter 038, 0.993
iter 039, 0.994
iter 040, 0.994
iter 041, 0.994
iter 042, 0.994
iter 043, 0.995
iter 044, 0.994
iter 045, 0.994
iter 046, 0.994
iter 047, 0.995
iter 048, 0.994
iter 049, 0.994
iter 050, 0.995
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