图片版权所有:art_inthecity
sudo pip install h5py
# MLP for Pima Indians Dataset Serialize to JSON and HDF5
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
import numpy
import os
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# 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, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10, verbose=0)
# evaluate the model
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# 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")
# evaluate loaded model on test data
loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
score = loaded_model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
acc: 78.78%
Saved model to disk
Loaded model from disk
acc: 78.78%
{
"keras_version":"2.0.2",
"backend":"theano",
"config":[
{
"config":{
"dtype":"float32",
"bias_regularizer":null,
"activation":"relu",
"bias_constraint":null,
"use_bias":true,
"bias_initializer":{
"config":{
},
"class_name":"Zeros"
},
"kernel_regularizer":null,
"activity_regularizer":null,
"kernel_constraint":null,
"trainable":true,
"name":"dense_1",
"kernel_initializer":{
"config":{
"maxval":0.05,
"minval":-0.05,
"seed":null
},
"class_name":"RandomUniform"
},
"batch_input_shape":[
null,
8
],
"units":12
},
"class_name":"Dense"
},
{
"config":{
"kernel_regularizer":null,
"bias_regularizer":null,
"activation":"relu",
"bias_constraint":null,
"use_bias":true,
"bias_initializer":{
"config":{
},
"class_name":"Zeros"
},
"activity_regularizer":null,
"kernel_constraint":null,
"trainable":true,
"name":"dense_2",
"kernel_initializer":{
"config":{
"maxval":0.05,
"minval":-0.05,
"seed":null
},
"class_name":"RandomUniform"
},
"units":8
},
"class_name":"Dense"
},
{
"config":{
"kernel_regularizer":null,
"bias_regularizer":null,
"activation":"sigmoid",
"bias_constraint":null,
"use_bias":true,
"bias_initializer":{
"config":{
},
"class_name":"Zeros"
},
"activity_regularizer":null,
"kernel_constraint":null,
"trainable":true,
"name":"dense_3",
"kernel_initializer":{
"config":{
"maxval":0.05,
"minval":-0.05,
"seed":null
},
"class_name":"RandomUniform"
},
"units":1
},
"class_name":"Dense"
}
],
"class_name":"Sequential"
}
# MLP for Pima Indians Dataset serialize to YAML and HDF5
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_yaml
import numpy
import os
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# 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, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10, verbose=0)
# evaluate the model
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# 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")
# evaluate loaded model on test data
loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
score = loaded_model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
acc: 78.78%
Saved model to disk
Loaded model from disk
acc: 78.78%
backend: theano
class_name: Sequential
config:
- class_name: Dense
config:
activation: relu
activity_regularizer: null
batch_input_shape: !!python/tuple [null, 8]
bias_constraint: null
bias_initializer:
class_name: Zeros
config: {}
bias_regularizer: null
dtype: float32
kernel_constraint: null
kernel_initializer:
class_name: RandomUniform
config: {maxval: 0.05, minval: -0.05, seed: null}
kernel_regularizer: null
name: dense_1
trainable: true
units: 12
use_bias: true
- class_name: Dense
config:
activation: relu
activity_regularizer: null
bias_constraint: null
bias_initializer:
class_name: Zeros
config: {}
bias_regularizer: null
kernel_constraint: null
kernel_initializer:
class_name: RandomUniform
config: {maxval: 0.05, minval: -0.05, seed: null}
kernel_regularizer: null
name: dense_2
trainable: true
units: 8
use_bias: true
- class_name: Dense
config:
activation: sigmoid
activity_regularizer: null
bias_constraint: null
bias_initializer:
class_name: Zeros
config: {}
bias_regularizer: null
kernel_constraint: null
kernel_initializer:
class_name: RandomUniform
config: {maxval: 0.05, minval: -0.05, seed: null}
kernel_regularizer: null
name: dense_3
trainable: true
units: 1
use_bias: true
keras_version: 2.0.2