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使用DBN识别手写体
传统的多层感知机或者神经网络的一个问题： 反向传播可能总是导致局部最小值。
当误差表面(error surface)包含了多个凹槽，当你做梯度下降时，你找到的并不是最深的凹槽。 下面你将会看到DBN是怎么解决这个问题的。
深度置信网络
深度置信网络可以通过额外的预训练规程解决局部最小值的问题。 预训练在反向传播之前做完，这样可以使错误率离最优的解不是那么远，也就是我们在最优解的附近。再通过反向传播慢慢地降低错误率。
深度置信网络主要分成两部分。第一部分是多层玻尔兹曼感知机，用于预训练我们的网络。第二部分是前馈反向传播网络，这可以使RBM堆叠的网络更加精细化。
1. 加载必要的深度置信网络库
# urllib is used to download the utils file from deeplearning.net
import urllib.request
response = urllib.request.urlopen('http://deeplearning.net/tutorial/code/utils.py')
content = response.read().decode('utf-8')
target = open('utils.py', 'w')
target.write(content)
target.close()
# Import the math function for calculations
import math
# Tensorflow library. Used to implement machine learning models
import tensorflow as tf
# Numpy contains helpful functions for efficient mathematical calculations
import numpy as np
# Image library for image manipulation
from PIL import Image
# import Image
# Utils file
from utils import tile_raster_images
2. 构建RBM层
RBM的细节参考【受限玻尔兹曼机(RBM)与python在Tensorflow的实现_青年夏日科技的博客-CSDN博客_python玻尔兹曼机】
为了在Tensorflow中应用DBN, 下面创建一个RBM的类
class RBM(object):
def __init__(self, input_size, output_size):
# Defining the hyperparameters
self._input_size = input_size # Size of input
self._output_size = output_size # Size of output
self.epochs = 5 # Amount of training iterations
self.learning_rate = 1.0 # The step used in gradient descent
self.batchsize = 100 # The size of how much data will be used for training per sub iteration
# Initializing weights and biases as matrices full of zeroes
self.w = np.zeros([input_size, output_size], np.float32) # Creates and initializes the weights with 0
self.hb = np.zeros([output_size], np.float32) # Creates and initializes the hidden biases with 0
self.vb = np.zeros([input_size], np.float32) # Creates and initializes the visible biases with 0
# Fits the result from the weighted visible layer plus the bias into a sigmoid curve
def prob_h_given_v(self, visible, w, hb):
# Sigmoid
return tf.nn.sigmoid(tf.matmul(visible, w) + hb)
# Fits the result from the weighted hidden layer plus the bias into a sigmoid curve
def prob_v_given_h(self, hidden, w, vb):
return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(w)) + vb)
# Generate the sample probability
def sample_prob(self, probs):
return tf.nn.relu(tf.sign(probs - tf.random_uniform(tf.shape(probs))))
# Training method for the model
def train(self, X):
# Create the placeholders for our parameters
_w = tf.placeholder("float", [self._input_size, self._output_size])
_hb = tf.placeholder("float", [self._output_size])
_vb = tf.placeholder("float", [self._input_size])
prv_w = np.zeros([self._input_size, self._output_size],
np.float32) # Creates and initializes the weights with 0
prv_hb = np.zeros([self._output_size], np.float32) # Creates and initializes the hidden biases with 0
prv_vb = np.zeros([self._input_size], np.float32) # Creates and initializes the visible biases with 0
cur_w = np.zeros([self._input_size, self._output_size], np.float32)
cur_hb = np.zeros([self._output_size], np.float32)
cur_vb = np.zeros([self._input_size], np.float32)
v0 = tf.placeholder("float", [None, self._input_size])
# Initialize with sample probabilities
h0 = self.sample_prob(self.prob_h_given_v(v0, _w, _hb))
v1 = self.sample_prob(self.prob_v_given_h(h0, _w, _vb))
h1 = self.prob_h_given_v(v1, _w, _hb)
# Create the Gradients
positive_grad = tf.matmul(tf.transpose(v0), h0)
negative_grad = tf.matmul(tf.transpose(v1), h1)
# Update learning rates for the layers
update_w = _w + self.learning_rate * (positive_grad - negative_grad) / tf.to_float(tf.shape(v0)[0])
update_vb = _vb + self.learning_rate * tf.reduce_mean(v0 - v1, 0)
update_hb = _hb + self.learning_rate * tf.reduce_mean(h0 - h1, 0)
# Find the error rate
err = tf.reduce_mean(tf.square(v0 - v1))
# Training loop
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# For each epoch
for epoch in range(self.epochs):
# For each step/batch
for start, end in zip(range(0, len(X), self.batchsize), range(self.batchsize, len(X), self.batchsize)):
batch = X[start:end]
# Update the rates
cur_w = sess.run(update_w, feed_dict={v0: batch, _w: prv_w, _hb: prv_hb, _vb: prv_vb})
cur_hb = sess.run(update_hb, feed_dict={v0: batch, _w: prv_w, _hb: prv_hb, _vb: prv_vb})
cur_vb = sess.run(update_vb, feed_dict={v0: batch, _w: prv_w, _hb: prv_hb, _vb: prv_vb})
prv_w = cur_w
prv_hb = cur_hb
prv_vb = cur_vb
error = sess.run(err, feed_dict={v0: X, _w: cur_w, _vb: cur_vb, _hb: cur_hb})
print('Epoch: %d' % epoch, 'reconstruction error: %f' % error)
self.w = prv_w
self.hb = prv_hb
self.vb = prv_vb
# Create expected output for our DBN
def rbm_outpt(self, X):
input_X = tf.constant(X)
_w = tf.constant(self.w)
_hb = tf.constant(self.hb)
out = tf.nn.sigmoid(tf.matmul(input_X, _w) + _hb)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
return sess.run(out)
3. 导入MNIST数据
使用one-hot encoding标注的形式载入MNIST图像数据。
# Getting the MNIST data provided by Tensorflow
from tensorflow.examples.tutorials.mnist import input_data
# Loading in the mnist data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images,\
mnist.test.labels
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
4. 建立DBN
RBM_hidden_sizes = [500, 200 , 50 ] #create 4 layers of RBM with size 785-500-200-50
#Since we are training, set input as training data
inpX = trX
#Create list to hold our RBMs
rbm_list = []
#Size of inputs is the number of inputs in the training set
input_size = inpX.shape[1]
#For each RBM we want to generate
for i, size in enumerate(RBM_hidden_sizes):
print('RBM: ',i,' ',input_size,'->', size)
rbm_list.append(RBM(input_size, size))
input_size = size
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
RBM: 0 784 -> 500
RBM: 1 500 -> 200
RBM: 2 200 -> 50rbm的类创建好了和数据都已经载入，可以创建DBN。 在这个例子中，我们使用了3个RBM，一个的隐藏层单元个数为500， 第二个RBM的隐藏层个数为200，最后一个为50. 我们想要生成训练数据的深层次表示形式。
5.训练RBM
我们将使用***rbm.train()***开始预训练步骤, 单独训练堆中的每一个RBM，并将当前RBM的输出作为下一个RBM的输入。
#For each RBM in our list
for rbm in rbm_list:
print('New RBM:')
#Train a new one
rbm.train(inpX)
#Return the output layer
inpX = rbm.rbm_outpt(inpX)
New RBM:
Epoch: 0 reconstruction error: 0.061174
Epoch: 1 reconstruction error: 0.052962
Epoch: 2 reconstruction error: 0.049679
Epoch: 3 reconstruction error: 0.047683
Epoch: 4 reconstruction error: 0.045691
New RBM:
Epoch: 0 reconstruction error: 0.035260
Epoch: 1 reconstruction error: 0.030811
Epoch: 2 reconstruction error: 0.028873
Epoch: 3 reconstruction error: 0.027428
Epoch: 4 reconstruction error: 0.026980
New RBM:
Epoch: 0 reconstruction error: 0.059593
Epoch: 1 reconstruction error: 0.056837
Epoch: 2 reconstruction error: 0.055571
Epoch: 3 reconstruction error: 0.053817
Epoch: 4 reconstruction error: 0.054142现在我们可以将输入数据的学习好的表示转换为有监督的预测，比如一个线性分类器。特别地，我们使用这个浅层神经网络的最后一层的输出对数字分类。
6. 神经网络
下面的类使用了上面预训练好的RBMs实现神经网络。
import numpy as np
import math
import tensorflow as tf
class NN(object):
def __init__(self, sizes, X, Y):
# Initialize hyperparameters
self._sizes = sizes
self._X = X
self._Y = Y
self.w_list = []
self.b_list = []
self._learning_rate = 1.0
self._momentum = 0.0
self._epoches = 10
self._batchsize = 100
input_size = X.shape[1]
# initialization loop
for size in self._sizes + [Y.shape[1]]:
# Define upper limit for the uniform distribution range
max_range = 4 * math.sqrt(6. / (input_size + size))
# Initialize weights through a random uniform distribution
self.w_list.append(
np.random.uniform(-max_range, max_range, [input_size, size]).astype(np.float32))
# Initialize bias as zeroes
self.b_list.append(np.zeros([size], np.float32))
input_size = size
# load data from rbm
def load_from_rbms(self, dbn_sizes, rbm_list):
# Check if expected sizes are correct
assert len(dbn_sizes) == len(self._sizes)
for i in range(len(self._sizes)):
# Check if for each RBN the expected sizes are correct
assert dbn_sizes[i] == self._sizes[i]
# If everything is correct, bring over the weights and biases
for i in range(len(self._sizes)):
self.w_list[i] = rbm_list[i].w
self.b_list[i] = rbm_list[i].hb
# Training method
def train(self):
# Create placeholders for input, weights, biases, output
_a = [None] * (len(self._sizes) + 2)
_w = [None] * (len(self._sizes) + 1)
_b = [None] * (len(self._sizes) + 1)
_a[0] = tf.placeholder("float", [None, self._X.shape[1]])
y = tf.placeholder("float", [None, self._Y.shape[1]])
# Define variables and activation functoin
for i in range(len(self._sizes) + 1):
_w[i] = tf.Variable(self.w_list[i])
_b[i] = tf.Variable(self.b_list[i])
for i in range(1, len(self._sizes) + 2):
_a[i] = tf.nn.sigmoid(tf.matmul(_a[i - 1], _w[i - 1]) + _b[i - 1])
# Define the cost function
cost = tf.reduce_mean(tf.square(_a[-1] - y))
# Define the training operation (Momentum Optimizer minimizing the Cost function)
train_op = tf.train.MomentumOptimizer(
self._learning_rate, self._momentum).minimize(cost)
# Prediction operation
predict_op = tf.argmax(_a[-1], 1)
# Training Loop
with tf.Session() as sess:
# Initialize Variables
sess.run(tf.global_variables_initializer())
# For each epoch
for i in range(self._epoches):
# For each step
for start, end in zip(
range(0, len(self._X), self._batchsize), range(self._batchsize, len(self._X), self._batchsize)):
# Run the training operation on the input data
sess.run(train_op, feed_dict={
_a[0]: self._X[start:end], y: self._Y[start:end]})
for j in range(len(self._sizes) + 1):
# Retrieve weights and biases
self.w_list[j] = sess.run(_w[j])
self.b_list[j] = sess.run(_b[j])
print("Accuracy rating for epoch " + str(i) + ": " + str(np.mean(np.argmax(self._Y, axis=1) == \
sess.run(predict_op, feed_dict={_a[0]: self._X, y: self._Y}))))
7. 运行
nNet = NN(RBM_hidden_sizes, trX, trY)
nNet.load_from_rbms(RBM_hidden_sizes,rbm_list)
nNet.train()
Accuracy rating for epoch 0: 0.46683636363636366
Accuracy rating for epoch 1: 0.6561272727272728
Accuracy rating for epoch 2: 0.7678363636363637
Accuracy rating for epoch 3: 0.8370727272727273
Accuracy rating for epoch 4: 0.8684181818181819
Accuracy rating for epoch 5: 0.885
Accuracy rating for epoch 6: 0.8947636363636363
Accuracy rating for epoch 7: 0.9024909090909091
Accuracy rating for epoch 8: 0.9080363636363636
Accuracy rating for epoch 9: 0.9124181818181818完整代码
pip install tensorflow==1.13.1
# Import the math function for calculations
import math
# Tensorflow library. Used to implement machine learning models
import tensorflow as tf
# Numpy contains helpful functions for efficient mathematical calculations
import numpy as np
# Image library for image manipulation
# import Image
# Utils file
# Getting the MNIST data provided by Tensorflow
from tensorflow.examples.tutorials.mnist import input_data
""" This file contains different utility functions that are not connected
in anyway to the networks presented in the tutorials, but rather help in
processing the outputs into a more understandable way.
For example ``tile_raster_images`` helps in generating a easy to grasp
image from a set of samples or weights.
"""
import numpy
def scale_to_unit_interval(ndar, eps=1e-8):
""" Scales all values in the ndarray ndar to be between 0 and 1 """
ndar = ndar.copy()
ndar -= ndar.min()
ndar *= 1.0 / (ndar.max() + eps)
return ndar
def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),
scale_rows_to_unit_interval=True,
output_pixel_vals=True):
"""
Transform an array with one flattened image per row, into an array in
which images are reshaped and layed out like tiles on a floor.
This function is useful for visualizing datasets whose rows are images,
and also columns of matrices for transforming those rows
(such as the first layer of a neural net).
:type X: a 2-D ndarray or a tuple of 4 channels, elements of which can
be 2-D ndarrays or None;
:param X: a 2-D array in which every row is a flattened image.
:type img_shape: tuple; (height, width)
:param img_shape: the original shape of each image
:type tile_shape: tuple; (rows, cols)
:param tile_shape: the number of images to tile (rows, cols)
:param output_pixel_vals: if output should be pixel values (i.e. int8
values) or floats
:param scale_rows_to_unit_interval: if the values need to be scaled before
being plotted to [0,1] or not
:returns: array suitable for viewing as an image.
(See:`Image.fromarray`.)
:rtype: a 2-d array with same dtype as X.
"""
assert len(img_shape) == 2
assert len(tile_shape) == 2
assert len(tile_spacing) == 2
# The expression below can be re-written in a more C style as
# follows :
#
# out_shape = [0,0]
# out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -
# tile_spacing[0]
# out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -
# tile_spacing[1]
out_shape = [
(ishp + tsp) * tshp - tsp
for ishp, tshp, tsp in zip(img_shape, tile_shape, tile_spacing)
]
if isinstance(X, tuple):
assert len(X) == 4
# Create an output numpy ndarray to store the image
if output_pixel_vals:
out_array = numpy.zeros((out_shape[0], out_shape[1], 4),
dtype='uint8')
else:
out_array = numpy.zeros((out_shape[0], out_shape[1], 4),
dtype=X.dtype)
#colors default to 0, alpha defaults to 1 (opaque)
if output_pixel_vals:
channel_defaults = [0, 0, 0, 255]
else:
channel_defaults = [0., 0., 0., 1.]
for i in range(4):
if X[i] is None:
# if channel is None, fill it with zeros of the correct
# dtype
dt = out_array.dtype
if output_pixel_vals:
dt = 'uint8'
out_array[:, :, i] = numpy.zeros(
out_shape,
dtype=dt
) + channel_defaults[i]
else:
# use a recurrent call to compute the channel and store it
# in the output
out_array[:, :, i] = tile_raster_images(
X[i], img_shape, tile_shape, tile_spacing,
scale_rows_to_unit_interval, output_pixel_vals)
return out_array
else:
# if we are dealing with only one channel
H, W = img_shape
Hs, Ws = tile_spacing
# generate a matrix to store the output
dt = X.dtype
if output_pixel_vals:
dt = 'uint8'
out_array = numpy.zeros(out_shape, dtype=dt)
for tile_row in range(tile_shape[0]):
for tile_col in range(tile_shape[1]):
if tile_row * tile_shape[1] + tile_col < X.shape[0]:
this_x = X[tile_row * tile_shape[1] + tile_col]
if scale_rows_to_unit_interval:
# if we should scale values to be between 0 and 1
# do this by calling the `scale_to_unit_interval`
# function
this_img = scale_to_unit_interval(
this_x.reshape(img_shape))
else:
this_img = this_x.reshape(img_shape)
# add the slice to the corresponding position in the
# output array
c = 1
if output_pixel_vals:
c = 255
out_array[
tile_row * (H + Hs): tile_row * (H + Hs) + H,
tile_col * (W + Ws): tile_col * (W + Ws) + W
] = this_img * c
return out_array
# Class that defines the behavior of the RBM
class RBM(object):
def __init__(self, input_size, output_size):
# Defining the hyperparameters
self._input_size = input_size # Size of input
self._output_size = output_size # Size of output
self.epochs = 5 # Amount of training iterations
self.learning_rate = 1.0 # The step used in gradient descent
self.batchsize = 100 # The size of how much data will be used for training per sub iteration
# Initializing weights and biases as matrices full of zeroes
self.w = np.zeros([input_size, output_size], np.float32) # Creates and initializes the weights with 0
self.hb = np.zeros([output_size], np.float32) # Creates and initializes the hidden biases with 0
self.vb = np.zeros([input_size], np.float32) # Creates and initializes the visible biases with 0
# Fits the result from the weighted visible layer plus the bias into a sigmoid curve
def prob_h_given_v(self, visible, w, hb):
# Sigmoid
return tf.nn.sigmoid(tf.matmul(visible, w) + hb)
# Fits the result from the weighted hidden layer plus the bias into a sigmoid curve
def prob_v_given_h(self, hidden, w, vb):
return tf.nn.sigmoid(tf.matmul(hidden, tf.transpose(w)) + vb)
# Generate the sample probability
def sample_prob(self, probs):
return tf.nn.relu(tf.sign(probs - tf.random_uniform(tf.shape(probs))))
# Training method for the model
def train(self, X):
# Create the placeholders for our parameters
_w = tf.placeholder("float", [self._input_size, self._output_size])
_hb = tf.placeholder("float", [self._output_size])
_vb = tf.placeholder("float", [self._input_size])
prv_w = np.zeros([self._input_size, self._output_size],
np.float32) # Creates and initializes the weights with 0
prv_hb = np.zeros([self._output_size], np.float32) # Creates and initializes the hidden biases with 0
prv_vb = np.zeros([self._input_size], np.float32) # Creates and initializes the visible biases with 0
cur_w = np.zeros([self._input_size, self._output_size], np.float32)
cur_hb = np.zeros([self._output_size], np.float32)
cur_vb = np.zeros([self._input_size], np.float32)
v0 = tf.placeholder("float", [None, self._input_size])
# Initialize with sample probabilities
h0 = self.sample_prob(self.prob_h_given_v(v0, _w, _hb))
v1 = self.sample_prob(self.prob_v_given_h(h0, _w, _vb))
h1 = self.prob_h_given_v(v1, _w, _hb)
# Create the Gradients
positive_grad = tf.matmul(tf.transpose(v0), h0)
negative_grad = tf.matmul(tf.transpose(v1), h1)
# Update learning rates for the layers
update_w = _w + self.learning_rate * (positive_grad - negative_grad) / tf.to_float(tf.shape(v0)[0])
update_vb = _vb + self.learning_rate * tf.reduce_mean(v0 - v1, 0)
update_hb = _hb + self.learning_rate * tf.reduce_mean(h0 - h1, 0)
# Find the error rate
err = tf.reduce_mean(tf.square(v0 - v1))
# Training loop
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# For each epoch
for epoch in range(self.epochs):
# For each step/batch
for start, end in zip(range(0, len(X), self.batchsize), range(self.batchsize, len(X), self.batchsize)):
batch = X[start:end]
# Update the rates
cur_w = sess.run(update_w, feed_dict={v0: batch, _w: prv_w, _hb: prv_hb, _vb: prv_vb})
cur_hb = sess.run(update_hb, feed_dict={v0: batch, _w: prv_w, _hb: prv_hb, _vb: prv_vb})
cur_vb = sess.run(update_vb, feed_dict={v0: batch, _w: prv_w, _hb: prv_hb, _vb: prv_vb})
prv_w = cur_w
prv_hb = cur_hb
prv_vb = cur_vb
error = sess.run(err, feed_dict={v0: X, _w: cur_w, _vb: cur_vb, _hb: cur_hb})
print('Epoch: %d' % epoch, 'reconstruction error: %f' % error)
self.w = prv_w
self.hb = prv_hb
self.vb = prv_vb
# Create expected output for our DBN
def rbm_outpt(self, X):
input_X = tf.constant(X)
_w = tf.constant(self.w)
_hb = tf.constant(self.hb)
out = tf.nn.sigmoid(tf.matmul(input_X, _w) + _hb)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
return sess.run(out)
class NN(object):
def __init__(self, sizes, X, Y):
# Initialize hyperparameters
self._sizes = sizes
self._X = X
self._Y = Y
self.w_list = []
self.b_list = []
self._learning_rate = 1.0
self._momentum = 0.0
self._epoches = 10
self._batchsize = 100
input_size = X.shape[1]
# initialization loop
for size in self._sizes + [Y.shape[1]]:
# Define upper limit for the uniform distribution range
max_range = 4 * math.sqrt(6. / (input_size + size))
# Initialize weights through a random uniform distribution
self.w_list.append(
np.random.uniform(-max_range, max_range, [input_size, size]).astype(np.float32))
# Initialize bias as zeroes
self.b_list.append(np.zeros([size], np.float32))
input_size = size
# load data from rbm
def load_from_rbms(self, dbn_sizes, rbm_list):
# Check if expected sizes are correct
assert len(dbn_sizes) == len(self._sizes)
for i in range(len(self._sizes)):
# Check if for each RBN the expected sizes are correct
assert dbn_sizes[i] == self._sizes[i]
# If everything is correct, bring over the weights and biases
for i in range(len(self._sizes)):
self.w_list[i] = rbm_list[i].w
self.b_list[i] = rbm_list[i].hb
# Training method
def train(self):
# Create placeholders for input, weights, biases, output
_a = [None] * (len(self._sizes) + 2)
_w = [None] * (len(self._sizes) + 1)
_b = [None] * (len(self._sizes) + 1)
_a[0] = tf.placeholder("float", [None, self._X.shape[1]])
y = tf.placeholder("float", [None, self._Y.shape[1]])
# Define variables and activation functoin
for i in range(len(self._sizes) + 1):
_w[i] = tf.Variable(self.w_list[i])
_b[i] = tf.Variable(self.b_list[i])
for i in range(1, len(self._sizes) + 2):
_a[i] = tf.nn.sigmoid(tf.matmul(_a[i - 1], _w[i - 1]) + _b[i - 1])
# Define the cost function
cost = tf.reduce_mean(tf.square(_a[-1] - y))
# Define the training operation (Momentum Optimizer minimizing the Cost function)
train_op = tf.train.MomentumOptimizer(
self._learning_rate, self._momentum).minimize(cost)
# Prediction operation
predict_op = tf.argmax(_a[-1], 1)
# Training Loop
with tf.Session() as sess:
# Initialize Variables
sess.run(tf.global_variables_initializer())
# For each epoch
for i in range(self._epoches):
# For each step
for start, end in zip(
range(0, len(self._X), self._batchsize), range(self._batchsize, len(self._X), self._batchsize)):
# Run the training operation on the input data
sess.run(train_op, feed_dict={
_a[0]: self._X[start:end], y: self._Y[start:end]})
for j in range(len(self._sizes) + 1):
# Retrieve weights and biases
self.w_list[j] = sess.run(_w[j])
self.b_list[j] = sess.run(_b[j])
print("Accuracy rating for epoch " + str(i) + ": " + str(np.mean(np.argmax(self._Y, axis=1) == \
sess.run(predict_op, feed_dict={_a[0]: self._X, y: self._Y}))))
if __name__ == '__main__':
# Loading in the mnist data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images,\
mnist.test.labels
RBM_hidden_sizes = [500, 200, 50] # create 4 layers of RBM with size 785-500-200-50
# Since we are training, set input as training data
inpX = trX
# Create list to hold our RBMs
rbm_list = []
# Size of inputs is the number of inputs in the training set
input_size = inpX.shape[1]
# For each RBM we want to generate
for i, size in enumerate(RBM_hidden_sizes):
print('RBM: ', i, ' ', input_size, '->', size)
rbm_list.append(RBM(input_size, size))
input_size = size
# For each RBM in our list
for rbm in rbm_list:
print('New RBM:')
# Train a new one
rbm.train(inpX)
# Return the output layer
inpX = rbm.rbm_outpt(inpX)
nNet = NN(RBM_hidden_sizes, trX, trY)
nNet.load_from_rbms(RBM_hidden_sizes, rbm_list)
nNet.train()任何程序错误，以及技术疑问或需要解答的，请添加
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