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Commit 08239a21 authored by Xiaofei Wang's avatar Xiaofei Wang
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from __future__ import absolute_import, division, print_function, unicode_literals
import random
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
class Net_model:
def train(self, x, y):
model = keras.models.Sequential([
keras.layers.Flatten(input_shape=(150, 6)),
keras.layers.Dense(1024, activation='relu'),
# keras.layers.Dropout(0.2),
keras.layers.Dense(150, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
train_num = len(x)//5*4
print('tn', train_num)
DATA_X = np.array(x)
DATA_Y = np.array(y)
TR_X, TE_X = DATA_X[:train_num], DATA_X[train_num:]
TR_Y, TE_Y = DATA_Y[:train_num], DATA_Y[train_num:]
cost = model.fit(TR_X, TR_Y, epochs=50)
print(cost)
cost = model.evaluate(TE_X, TE_Y)
print(cost)
Y_pred = model.predict(TE_X)
# print(TE_X[0])
# print(np.argmax(Y_pred[0]))
model.save('naive_model.h5')
def perdict(self, X):
model = keras.models.load_model('naive_model.h5')
Y_pred = model.predict(X)
return Y_pred
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