## https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/
# Visualize training history
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
import numpy
import requests
import csv
import urllib.request
import pandas as pd
## https://stackoverflow.com/a/36162600/3806250
## https://stackoverflow.com/a/18897408/3806250
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv'
response = requests.get(url)
response.iter_lines()
text = response.iter_lines()
# reader = csv.reader(text, delimiter = ',')
reader = numpy.loadtxt(text, delimiter = ',')
# data = [row for row in reader]
# load pima indians dataset
dataset = reader
pd.DataFrame(dataset)
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
history = model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, verbose=0)
# list all data in history
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
连接来源：Display Deep Learning Model Training History in Keras
太多不统一，使用个
