[Keras] Develop Neural Network With Keras Step-By-Step

简单地训练一个四层全连接网络。

Ref: http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/

1. Load Data

下载　　：Data download --> 保存为：pima-indians-diabetes.csv

from keras.models import Sequential

from keras.layers import Dense

import numpy

# fix random seed for reproducibility

seed = 7

numpy.random.seed(seed)

# 1.播了个随机种子

# load pima indians dataset

dataset = ("pima-indians-diabetes.csv", delimiter=",")

# split into input (X) and output (Y) variables

X = dataset[:,0:8]

Y = dataset[:,8]
# 2.读取了数据放在了二维数组

2. Define Model

目标：采用四层全连接网络

思路：采用Sequential model，然后一次添加一个layers：

- 参数1：一层网络的结点个数【第一层（input） 8个 --> 第二层 12个 --> 第三层 8个 --> 第四层（output） 1个】
- 参数2：初始化方法　　【0-0.05均匀分布】
- 参数3：激活函数【第二、三层：relu；第四层：sigmoid】

# create model

model = Sequential()

model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))

model.add(Dense(8, init='uniform', activation='relu'))

model.add(Dense(1, init='uniform', activation='sigmoid'))

3. Compile Model

我们的目的：找较好的权重w来做预测。

- 损失函数：logarithmic loss
- 逼近模型：efficient gradient descent algorithm

# Compile model

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

Finally, because it is a classification problem, we will collect and report the classification accuracy as the metric.

Otherwise，见<6. load model>，也可直接导入现成模型，继续训练。

4. Fit Model

开始训练数据，监督学习：

- 循环次数：epoch
- 批统计量：batch

# Fit the model

history_callback = model.fit(X, Y, nb_epoch=150, batch_size=10)

5. Evaluate Model

训练后，使用model.evaluate(...)预测成功率统计：

# evaluate the model

scores = model.evaluate(X, Y)

print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

运行结果：acc: 78.91%

Epoch 1/150

768/768 [==============================] - 0s - loss: 0.6826 - acc: 0.6328

Epoch 2/150

768/768 [==============================] - 0s - loss: 0.6590 - acc: 0.6510

Epoch 3/150

768/768 [==============================] - 0s - loss: 0.6475 - acc: 0.6549

Epoch 4/150

768/768 [==============================] - 0s - loss: 0.6416 - acc: 0.6615

Epoch 5/150

768/768 [==============================] - 0s - loss: 0.6216 - acc: 0.6745

Epoch 6/150

768/768 [==============================] - 0s - loss: 0.6128 - acc: 0.6680

Epoch 7/150

768/768 [==============================] - 0s - loss: 0.6018 - acc: 0.6927

Epoch 8/150

768/768 [==============================] - 0s - loss: 0.5962 - acc: 0.6927

Epoch 9/150

768/768 [==============================] - 0s - loss: 0.5991 - acc: 0.6953

Epoch 10/150

768/768 [==============================] - 0s - loss: 0.5920 - acc: 0.6927

Epoch 11/150

768/768 [==============================] - 0s - loss: 0.5905 - acc: 0.6979

Epoch 12/150

768/768 [==============================] - 0s - loss: 0.5883 - acc: 0.6901

Epoch 13/150

768/768 [==============================] - 0s - loss: 0.5870 - acc: 0.6953

Epoch 14/150

768/768 [==============================] - 0s - loss: 0.5869 - acc: 0.6836

Epoch 15/150

768/768 [==============================] - 0s - loss: 0.5815 - acc: 0.6953

Epoch 16/150

768/768 [==============================] - 0s - loss: 0.5779 - acc: 0.6966

Epoch 17/150

768/768 [==============================] - 0s - loss: 0.5809 - acc: 0.6849

Epoch 18/150

768/768 [==============================] - 0s - loss: 0.5818 - acc: 0.6953

Epoch 19/150

768/768 [==============================] - 0s - loss: 0.5814 - acc: 0.6901

Epoch 20/150

768/768 [==============================] - 0s - loss: 0.5748 - acc: 0.7096

Epoch 21/150

768/768 [==============================] - 0s - loss: 0.5758 - acc: 0.7005

Epoch 22/150

768/768 [==============================] - 0s - loss: 0.5739 - acc: 0.7135

Epoch 23/150

768/768 [==============================] - 0s - loss: 0.5736 - acc: 0.6927

Epoch 24/150

768/768 [==============================] - 0s - loss: 0.5750 - acc: 0.6940

Epoch 25/150

768/768 [==============================] - 0s - loss: 0.5734 - acc: 0.7031

Epoch 26/150

768/768 [==============================] - 0s - loss: 0.5683 - acc: 0.7083

Epoch 27/150

768/768 [==============================] - 0s - loss: 0.5688 - acc: 0.7018

Epoch 28/150

768/768 [==============================] - 0s - loss: 0.5714 - acc: 0.7070

Epoch 29/150

768/768 [==============================] - 0s - loss: 0.5621 - acc: 0.7188

Epoch 30/150

768/768 [==============================] - 0s - loss: 0.5647 - acc: 0.7122

Epoch 31/150

768/768 [==============================] - 0s - loss: 0.5630 - acc: 0.7135

Epoch 32/150

768/768 [==============================] - 0s - loss: 0.5613 - acc: 0.7214

Epoch 33/150

768/768 [==============================] - 0s - loss: 0.5594 - acc: 0.7188

Epoch 34/150

768/768 [==============================] - 0s - loss: 0.5598 - acc: 0.7187

Epoch 35/150

768/768 [==============================] - 0s - loss: 0.5624 - acc: 0.7187

Epoch 36/150

768/768 [==============================] - 0s - loss: 0.5615 - acc: 0.7201

Epoch 37/150

768/768 [==============================] - 0s - loss: 0.5544 - acc: 0.7214

Epoch 38/150

768/768 [==============================] - 0s - loss: 0.5529 - acc: 0.7135

Epoch 39/150

768/768 [==============================] - 0s - loss: 0.5550 - acc: 0.7227

Epoch 40/150

768/768 [==============================] - 0s - loss: 0.5574 - acc: 0.7331

Epoch 41/150

768/768 [==============================] - 0s - loss: 0.5561 - acc: 0.7357

Epoch 42/150

768/768 [==============================] - 0s - loss: 0.5459 - acc: 0.7370

Epoch 43/150

768/768 [==============================] - 0s - loss: 0.5481 - acc: 0.7240

Epoch 44/150

768/768 [==============================] - 0s - loss: 0.5409 - acc: 0.7331

Epoch 45/150

768/768 [==============================] - 0s - loss: 0.5438 - acc: 0.7422

Epoch 46/150

768/768 [==============================] - 0s - loss: 0.5360 - acc: 0.7344

Epoch 47/150

768/768 [==============================] - 0s - loss: 0.5393 - acc: 0.7357

Epoch 48/150

768/768 [==============================] - 0s - loss: 0.5360 - acc: 0.7435

Epoch 49/150

768/768 [==============================] - 0s - loss: 0.5407 - acc: 0.7370

Epoch 50/150

768/768 [==============================] - 0s - loss: 0.5473 - acc: 0.7344

Epoch 51/150

768/768 [==============================] - 0s - loss: 0.5287 - acc: 0.7448

Epoch 52/150

768/768 [==============================] - 0s - loss: 0.5283 - acc: 0.7539

Epoch 53/150

768/768 [==============================] - 0s - loss: 0.5308 - acc: 0.7396

Epoch 54/150

768/768 [==============================] - 0s - loss: 0.5274 - acc: 0.7448

Epoch 55/150

768/768 [==============================] - 0s - loss: 0.5241 - acc: 0.7539

Epoch 56/150

768/768 [==============================] - 0s - loss: 0.5262 - acc: 0.7526

Epoch 57/150

768/768 [==============================] - 0s - loss: 0.5272 - acc: 0.7422

Epoch 58/150

768/768 [==============================] - 0s - loss: 0.5262 - acc: 0.7539

Epoch 59/150

768/768 [==============================] - 0s - loss: 0.5224 - acc: 0.7604

Epoch 60/150

768/768 [==============================] - 0s - loss: 0.5200 - acc: 0.7513

Epoch 61/150

768/768 [==============================] - 0s - loss: 0.5158 - acc: 0.7578

Epoch 62/150

768/768 [==============================] - 0s - loss: 0.5162 - acc: 0.7513

Epoch 63/150

768/768 [==============================] - 0s - loss: 0.5097 - acc: 0.7552

Epoch 64/150

768/768 [==============================] - 0s - loss: 0.5134 - acc: 0.7487

Epoch 65/150

768/768 [==============================] - 0s - loss: 0.5112 - acc: 0.7435

Epoch 66/150

768/768 [==============================] - 0s - loss: 0.5141 - acc: 0.7656

Epoch 67/150

768/768 [==============================] - 0s - loss: 0.5082 - acc: 0.7539

Epoch 68/150

768/768 [==============================] - 0s - loss: 0.5101 - acc: 0.7643

Epoch 69/150

768/768 [==============================] - 0s - loss: 0.5136 - acc: 0.7409

Epoch 70/150

768/768 [==============================] - 0s - loss: 0.5182 - acc: 0.7474

Epoch 71/150

768/768 [==============================] - 0s - loss: 0.5185 - acc: 0.7370

Epoch 72/150

768/768 [==============================] - 0s - loss: 0.5073 - acc: 0.7539

Epoch 73/150

768/768 [==============================] - 0s - loss: 0.4982 - acc: 0.7682

Epoch 74/150

768/768 [==============================] - 0s - loss: 0.4967 - acc: 0.7591

Epoch 75/150

768/768 [==============================] - 0s - loss: 0.5070 - acc: 0.7617

Epoch 76/150

768/768 [==============================] - 0s - loss: 0.5025 - acc: 0.7526

Epoch 77/150

768/768 [==============================] - 0s - loss: 0.4991 - acc: 0.7604

Epoch 78/150

768/768 [==============================] - 0s - loss: 0.4923 - acc: 0.7656

Epoch 79/150

768/768 [==============================] - 0s - loss: 0.4998 - acc: 0.7695

Epoch 80/150

768/768 [==============================] - 0s - loss: 0.5004 - acc: 0.7526

Epoch 81/150

768/768 [==============================] - 0s - loss: 0.5043 - acc: 0.7552

Epoch 82/150

768/768 [==============================] - 0s - loss: 0.5002 - acc: 0.7656

Epoch 83/150

768/768 [==============================] - 0s - loss: 0.4932 - acc: 0.7617

Epoch 84/150

768/768 [==============================] - 0s - loss: 0.4971 - acc: 0.7604

Epoch 85/150

768/768 [==============================] - 0s - loss: 0.5007 - acc: 0.7513

Epoch 86/150

768/768 [==============================] - 0s - loss: 0.4889 - acc: 0.7656

Epoch 87/150

768/768 [==============================] - 0s - loss: 0.4953 - acc: 0.7591

Epoch 88/150

768/768 [==============================] - 0s - loss: 0.4910 - acc: 0.7669

Epoch 89/150

768/768 [==============================] - 0s - loss: 0.4897 - acc: 0.7604

Epoch 90/150

768/768 [==============================] - 0s - loss: 0.4867 - acc: 0.7643

Epoch 91/150

768/768 [==============================] - 0s - loss: 0.4915 - acc: 0.7669

Epoch 92/150

768/768 [==============================] - 0s - loss: 0.4907 - acc: 0.7630

Epoch 93/150

768/768 [==============================] - 0s - loss: 0.4912 - acc: 0.7604

Epoch 94/150

768/768 [==============================] - 0s - loss: 0.4851 - acc: 0.7630

Epoch 95/150

768/768 [==============================] - 0s - loss: 0.4821 - acc: 0.7682

Epoch 96/150

768/768 [==============================] - 0s - loss: 0.4835 - acc: 0.7669

Epoch 97/150

768/768 [==============================] - 0s - loss: 0.4738 - acc: 0.7773

Epoch 98/150

768/768 [==============================] - 0s - loss: 0.5008 - acc: 0.7474

Epoch 99/150

768/768 [==============================] - 0s - loss: 0.4841 - acc: 0.7682

Epoch 100/150

768/768 [==============================] - 0s - loss: 0.4816 - acc: 0.7669

Epoch 101/150

768/768 [==============================] - 0s - loss: 0.4843 - acc: 0.7695

Epoch 102/150

768/768 [==============================] - 0s - loss: 0.4753 - acc: 0.7891

Epoch 103/150

768/768 [==============================] - 0s - loss: 0.4841 - acc: 0.7630

Epoch 104/150

768/768 [==============================] - 0s - loss: 0.4836 - acc: 0.7786

Epoch 105/150

768/768 [==============================] - 0s - loss: 0.4809 - acc: 0.7708

Epoch 106/150

768/768 [==============================] - 0s - loss: 0.4792 - acc: 0.7786

Epoch 107/150

768/768 [==============================] - 0s - loss: 0.4831 - acc: 0.7734

Epoch 108/150

768/768 [==============================] - 0s - loss: 0.4783 - acc: 0.7852

Epoch 109/150

768/768 [==============================] - 0s - loss: 0.4784 - acc: 0.7708

Epoch 110/150

768/768 [==============================] - 0s - loss: 0.4803 - acc: 0.7682

Epoch 111/150

768/768 [==============================] - 0s - loss: 0.4704 - acc: 0.7734

Epoch 112/150

768/768 [==============================] - 0s - loss: 0.4752 - acc: 0.7878

Epoch 113/150

768/768 [==============================] - 0s - loss: 0.4776 - acc: 0.7760

Epoch 114/150

768/768 [==============================] - 0s - loss: 0.4849 - acc: 0.7604

Epoch 115/150

768/768 [==============================] - 0s - loss: 0.4773 - acc: 0.7682

Epoch 116/150

768/768 [==============================] - 0s - loss: 0.4712 - acc: 0.7773

Epoch 117/150

768/768 [==============================] - 0s - loss: 0.4675 - acc: 0.7786

Epoch 118/150

768/768 [==============================] - 0s - loss: 0.4660 - acc: 0.7839

Epoch 119/150

768/768 [==============================] - 0s - loss: 0.4702 - acc: 0.7891

Epoch 120/150

768/768 [==============================] - 0s - loss: 0.4699 - acc: 0.7852

Epoch 121/150

768/768 [==============================] - 0s - loss: 0.4786 - acc: 0.7852

Epoch 122/150

768/768 [==============================] - 0s - loss: 0.4745 - acc: 0.7786

Epoch 123/150

768/768 [==============================] - 0s - loss: 0.4684 - acc: 0.7839

Epoch 124/150

768/768 [==============================] - 0s - loss: 0.4709 - acc: 0.7760

Epoch 125/150

768/768 [==============================] - 0s - loss: 0.4699 - acc: 0.7747

Epoch 126/150

768/768 [==============================] - 0s - loss: 0.4649 - acc: 0.7747

Epoch 127/150

768/768 [==============================] - 0s - loss: 0.4709 - acc: 0.7708

Epoch 128/150

768/768 [==============================] - 0s - loss: 0.4573 - acc: 0.7982

Epoch 129/150

768/768 [==============================] - 0s - loss: 0.4646 - acc: 0.7943

Epoch 130/150

768/768 [==============================] - 0s - loss: 0.4775 - acc: 0.7773

Epoch 131/150

768/768 [==============================] - 0s - loss: 0.4613 - acc: 0.7799

Epoch 132/150

768/768 [==============================] - 0s - loss: 0.4608 - acc: 0.7799

Epoch 133/150

768/768 [==============================] - 0s - loss: 0.4737 - acc: 0.7826

Epoch 134/150

768/768 [==============================] - 0s - loss: 0.4711 - acc: 0.7773

Epoch 135/150

768/768 [==============================] - 0s - loss: 0.4665 - acc: 0.7839

Epoch 136/150

768/768 [==============================] - 0s - loss: 0.4579 - acc: 0.7969

Epoch 137/150

768/768 [==============================] - 0s - loss: 0.4621 - acc: 0.7917

Epoch 138/150

768/768 [==============================] - 0s - loss: 0.4684 - acc: 0.7760

Epoch 139/150

768/768 [==============================] - 0s - loss: 0.4597 - acc: 0.7839

Epoch 140/150

768/768 [==============================] - 0s - loss: 0.4593 - acc: 0.7799

Epoch 141/150

768/768 [==============================] - 0s - loss: 0.4624 - acc: 0.7799

Epoch 142/150

768/768 [==============================] - 0s - loss: 0.4609 - acc: 0.7786

Epoch 143/150

768/768 [==============================] - 0s - loss: 0.4648 - acc: 0.7826

Epoch 144/150

768/768 [==============================] - 0s - loss: 0.4541 - acc: 0.8060

Epoch 145/150

768/768 [==============================] - 0s - loss: 0.4597 - acc: 0.7852

Epoch 146/150

768/768 [==============================] - 0s - loss: 0.4639 - acc: 0.7891

Epoch 147/150

768/768 [==============================] - 0s - loss: 0.4548 - acc: 0.7865

Epoch 148/150

768/768 [==============================] - 0s - loss: 0.4659 - acc: 0.7786

Epoch 149/150

768/768 [==============================] - 0s - loss: 0.4596 - acc: 0.7799

Epoch 150/150

768/768 [==============================] - 0s - loss: 0.4615 - acc: 0.7773

 32/768 [>.............................] - ETA: 0sacc: 78.91%

log

6. Save & load model

分析log：How to log Keras loss output to a file

loss_history = history_callback.history["loss"]

acc_history  = history_callback.history["acc"]

Save and Load Your Keras Deep Learning Models

模型：model.json （option: json or yaml格式）
权重：model.h5

# serialize model to JSON

model_json = model.to_json()

with open("model.json", "w+") as json_file:

    json_file.write(model_json)

# serialize weights to HDF5

model.save_weights("model.h5")

print("Saved model to disk")

# later...

# load json and create model

json_file = open('model.json', 'r')

loaded_model_json = json_file.read()

json_file.close()

loaded_model = model_from_json(loaded_model_json)

# load weights into new model

loaded_model.load_weights("model.h5")

print("Loaded model from disk")

# serialize model to YAML

model_yaml = model.to_yaml()

with open("model.yaml", "w") as yaml_file:

    yaml_file.write(model_yaml)

# serialize weights to HDF5

model.save_weights("model.h5")

print("Saved model to disk")

# later...

# load YAML and create model

yaml_file = open('model.yaml', 'r')

loaded_model_yaml = yaml_file.read()

yaml_file.close()

loaded_model = model_from_yaml(loaded_model_yaml)

# load weights into new model

loaded_model.load_weights("model.h5")

print("Loaded model from disk")

7. Make Predictions

通过numpy.loadtxt(...) 获取新的数据，放入X中。

# calculate predictions

predictions = model.predict(X)

# round predictions

rounded = [int(round(x[0])) for x in predictions]

print(rounded)

附加题：Multilayer Perceptron (理解图与代码的对应关系)

Code: a Multilayer Perceptron

import numpy as np

np.random.seed(1337) # for reproducibility

import os

from keras.datasets import mnist　　　　#自动下载

from keras.models import Sequential　

from keras.layers.core import Dense, Dropout, Activation

from keras.optimizers import RMSprop

from keras.utils import np_utils

batch_size = 128 #Number of images used in each optimization step

nb_classes = 10 #One class per digit

nb_epoch = 12 #Number of times the whole data is used to learn

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

#Flatten the data: 神经网络不用二位数组作为数据，所以这里变为一维

X_train = X_train.reshape(60000, 784)

X_test  = X_test.reshape(10000, 784)

#Make the value floats in [0;1] instead of int in [0;255] --> [归一化]

X_train = X_train.astype('float32')

X_test  = X_test.astype('float32')

X_train /= 255

X_test  /= 255

#Display the shapes to check if everything's ok

print(X_train.shape[0], 'train samples')

print(X_test.shape[0],  'test samples')

# convert class vectors to binary class matrices (ie one-hot vectors)
Y_train = np_utils.to_categorical(y_train, nb_classes)

Y_test  = np_utils.to_categorical(y_test,  nb_classes)

#Define the model achitecture

model = Sequential()
########################################################################################

model.add(Dense(512, input_shape=(784,)))

model.add(Activation('relu'))

model.add(Dropout(0.2))

model.add(Dense(512))

model.add(Activation('relu'))

model.add(Dropout(0.2))

model.add(Dense(10)) #Last layer with one output per class 二值化表示

model.add(Activation('softmax')) #We want a score simlar to a probability for each class

########################################################################################

#Use rmsprop to do the gradient descent see http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf

#and http://cs231n.github.io/neural-networks-3/#ada

rms = RMSprop()　　# 随机梯度下降

#The function to optimize is the cross entropy between the true label and the output (softmax) of the model

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

 

#Make the model learn --> [Training]

model.fit(X_train, Y_train,

batch_size=batch_size, nb_epoch=nb_epoch,

verbose=2,

validation_data=(X_test, Y_test))

#Evaluate how the model does on the test set

score = model.evaluate(X_test, Y_test, verbose=0)

print('Test score:', score[0])

print('Test accuracy:', score[1])

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