2019-04-25 3层前向神经网络&输出层设计&手写数字识别

Day4

1.三层神经网络（forward propagation）

import numpy as np

from activation_functions import identity_function, sigmoid
from perceptron import Multi_Layered_Perceptron


class NeuralNetwork(Multi_Layered_Perceptron):

    network = {}

    def __init__(self):
        self.network['W1'] = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])
        self.network['b1'] = np.array([0.1, 0.2, 0.3])
        self.network['W2'] = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])
        self.network['b2'] = np.array([0.1, 0.2])
        self.network['W3'] = np.array([[0.1, 0.3], [0.2, 0.4]])
        self.network['b3'] = np.array([0.1, 0.2])

    def forward(self, x):
        W1, W2, W3 = self.network['W1'], self.network['W2'], self.network['W3']
        b1, b2, b3 = self.network['b1'], self.network['b2'], self.network['b3']

        a1 = np.dot(x, W1) + b1
        z1 = sigmoid(a1)
        a2 = np.dot(z1, W2) + b2
        z2 = sigmoid(a2)
        a3 = np.dot(z2, W3) + b3
        y = identity_function(a3)

        return y


network = NeuralNetwork()
x = np.array([1.0, 0.5])
y = network.forward(x)
print(y)

2.输出层的设计

3.手写数字图像识别

#!/usr/bin/env python
# coding: utf-8

# # import dependencies

# In[1]:


import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import keras as K

from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D
from keras.datasets import mnist
from keras.utils import np_utils

plt.rcParams['figure.figsize'] = (9, 9)
get_ipython().run_line_magic('matplotlib', 'inline')


# # load data

# In[2]:


(X_train, y_train), (X_test, y_test) = mnist.load_data()

print(X_train.shape)
print(X_test.shape)


# In[3]:


for i in range(9):
    plt.subplot(3, 3, i + 1)
    plt.imshow(X_train[i], cmap='gray', interpolation='none')
    plt.title('class {}'.format(y_train[i]))


# # format data to vectors

# In[4]:


X_train = X_train.reshape(60000, 784).astype('float32')
X_test = X_test.reshape(10000, 784).astype('float32')

#normalization
X_train /= 255
X_test /= 255

print(X_train.shape)
print(X_test.shape)


# # One-Hot Encoding target classes

# In[5]:


Y_train = np_utils.to_categorical(y_train, num_classes=10)
Y_test = np_utils.to_categorical(y_test, num_classes=10)


# # Building NN

# In[6]:


model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784, )))
model.add(Dropout(0.2))

model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(10, activation='softmax'))


# # Compile the model

# In[7]:


model.compile(
    loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])


# # Training

# In[8]:


model.fit(
    X_train,
    Y_train,
    batch_size=128,
    epochs=4,
    verbose=1,
    validation_data=(X_test, Y_test))


# # Evaluating

# In[9]:


score = model.evaluate(X_test, Y_test, verbose=0)
print('test score:', score[0])
print('test accuracy:', score[1])


# In[ ]: