Doppelkopf/initiate_model.py at master · jlittek/Doppelkopf · GitHub

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# from keras.models import Sequential
# from keras.layers import InputLayer
# from keras.layers import Dense
# from keras.optimizers import adam_v2

# model = Sequential()
# model.add(InputLayer(input_shape=(1,(24*4+3)*12)))
# model.add(Dense(100, activation='sigmoid'))
# model.add(Dense(100, activation='sigmoid'))
# model.add(Dense(50, activation='relu'))
# model.add(Dense(24, activation='linear'))
# model.compile(loss='mse', optimizer=adam_v2.Adam(learning_rate=1e-6), metrics=['mae'])

# model.save('./Doppelkopf/model_4_Spieler.h5')

# # from keras.models import load_model


# # model = load_model('./Doppelkopf/model.h5')
# # print(model.summary())

# import numpy as np

# a = np.empty((2,2))
# print(a)
# # print(np.append(a, np.array([1,2])))

# Standard CNN from Tensorflow:
import numpy as np
import tensorflow as tf

from keras import  layers, models

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(33, 36, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(24))

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-6, beta_1=0.9, beta_2=0.98,
                                     epsilon=1e-9),
              loss=tf.keras.losses.MeanSquaredError(),
              metrics=['mse'])

model.save('models/model_1.h5')