import mnist_loader import network # 训练集、验证集、测试集 training_data, validation_data, test_data = mnist_loader.load_data_wapper() print(type(training_data)) # 调用SGD随机梯度下降算法 network = network.Network([784, 30, 10]) network.SGD(training_data, 30, 20, 3.0, test_data=test_data)
# This is the second part of the processing unit, i.e. the neural network section.
# The i_vectors and o_vectors obtained from the file_parser section are combined together
# to generate the training data set for the neural network. The training data set is a three-
# dimensional numpy array, which has a structure like:
# [[[input array 1] [output array 1]]
# [[input array 2] [output array 2]]
# [[input array 3] [output array 3]]
# ...]
training_data = numpy.empty([int(len(i_vectors) - 1), 2], dtype=numpy.ndarray)
i = 0
for i_vector, o_vector in zip(i_vectors, o_vectors):
i_vector = i_vector[numpy.newaxis]
i_vector = i_vector.transpose()
o_vector = o_vector[numpy.newaxis]
o_vector = o_vector.transpose()
if i < len(training_data):
training_data[i, 0] = i_vector
training_data[i, 1] = o_vector
else:
test_data = i_vector
i = i + 1
# This section deals with instancing the Neural Network class (named Network) and calling its methods
# eta is the learning rate, layers_sizes is a list containg the number of neurons in each of the layers with
# first and last layer being the input and output vectors, respectively.
eta = 1 # Learning Rate
layers_sizes = [len(optypes), 200, len(atomtypes) * 2
] # Layer structure: Input Layer, Hidden Layer, Output Layer
network = Network1(layers_sizes)
network.SGD(training_data, 10, 8, eta, test_data)
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import mnist_loader
import network
import time
(training_data, validation_data, test_data) = mnist_loader.load_data_wrapper()
print "Load data finished."
network = network.Network([784, 30, 10])
print "Network initialize completed."
start = time.clock()
network.SGD(training_data,
epochs=30,
mini_batch_size=10,
eta=3.0,
test_data=validation_data)
elapsed = (time.clock() - start)
print("Training Time used:", elapsed)
print "Evaluate: {0} / {1}".format(network.evaluate(test_data), len(test_data))
])
#Setting up model structure
layers = [
net.Linear(in_kernel=784, out_kernel=200, bias=True),
net.Linear(200, 100, bias=True),
net.ReLU(),
net.Linear(100, 10, bias=True),
net.ReLU(),
net.Linear(10, 10, bias=True),
net.SoftMax(),
net.CrossEntropy()
]
#Init new SGD optimizer
optimizer = net.SGD(learning_rate=learning_rate)
#Intit new network
network = net.Network(layers=layers, optimizer=optimizer)
#Training Network
t0 = time.time()
print("Starting training process...")
history = network.train(x, y, batch_size, epochs, shuffle=True)
#Testing Network
print("Starting evaluation process...")
evaluation = network.evaluate(x_test, y_test, batch_size)
print("Total time elapsed: {} minutes".format((time.time() - t0) / 60))
#Plotting Training Loss