From 08239a2181993426412cc30fa09ef47438d88f55 Mon Sep 17 00:00:00 2001
From: Xiaofei Wang <xiaofei@student.unimelb.edu.au>
Date: Wed, 11 Mar 2020 21:04:04 +1100
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=B0=E6=96=87=E4=BB=B6?=
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---
AZUL Learn Opponent/learn_opponent.py | 48 +++++++++++++++++++++++++++
1 file changed, 48 insertions(+)
create mode 100644 AZUL Learn Opponent/learn_opponent.py
diff --git a/AZUL Learn Opponent/learn_opponent.py b/AZUL Learn Opponent/learn_opponent.py
new file mode 100644
index 0000000..e3ff9df
--- /dev/null
+++ b/AZUL Learn Opponent/learn_opponent.py
@@ -0,0 +1,48 @@
+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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