history['reward'].append(reward)
history['exploration'].append(ai_player.epsilon)
else:
reward = reward+0.05 if headed_towards_fruit else reward-0.05
next_state = np.array([convert_to_numbers(s) for s in next_state])\
.reshape(input_dims)
ai_player.memorize(state, action, reward, next_state, done)
state = next_state
BOARD.wipe_game()
# Retrain the snake using older states
ai_player.replay(400)
# Save every 1000 episodes
if e%1000 == 0:
ai_player.save_model('./model')
print('saving the model...')
print('saving the history to csv file...')
with open('./hist.csv', 'w') as f:
w = csv.DictWriter(f, history.keys())
w.writeheader()
w.writerow(history)
return history
if __name__=='__main__':
hist = train_snake(show_pygame=False, episodes=2000, epsilon_phase_size=0.66, num_last_frames=4)
plot_history(hist)
load_show_agent('./model', BOARD, 5)
(train_data, test_data), info = tfds.load('imdb_reviews/subwords8k',
split=(tfds.Split.TRAIN,
tfds.Split.TEST),
with_info=True,
as_supervised=True)
encoder = info.features['text'].encoder
train_batches = pad_sequences(train_data)
test_batches = pad_sequences(test_data)
embedding_dim = 16
model = Sequential([
Embedding(encoder.vocab_size, embedding_dim),
GlobalAveragePooling1D(),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(train_batches,
epochs=10,
validation_data=test_batches,
validation_steps=20)
save(model)
saveEmbedding(model.layers[0], info.features['text'].encoder)
plot_history(model.history.history)
optimizer=optimizers.Adam(lr=0.05, amsgrad=True),
loss=tf.losses.categorical_crossentropy,
metrics=['acc', auc]
)
history = model.fit(
train_gen,
epochs=MAX_EPOCHS,
validation_data=val_gen,
verbose=2,
shuffle=False,
callbacks=[es_callback]
)
model.summary()
plot_history(history, es_callback, f'Node Level GCN', cfg.STORAGE_BASE_THESIS_IMG + rf'\gcn_node.pdf')
# TEST MODEL ===========================================================================================================
all_predictions = model.predict(all_gen)
test_predictions = model.predict(test_gen)
all_node_predictions = target_encoding.inverse_transform(all_predictions.squeeze())
test_node_predictions = target_encoding.inverse_transform(test_predictions.squeeze())
all_predictions_df = pd.DataFrame({"Predicted": all_node_predictions,
"True": graph_labels,
"Fraud IDs": fraud_ids}, index=graph_labels.index)
all_predictions_df['is_correct'] = all_predictions_df['Predicted'] == all_predictions_df['True']
all_predictions_df.to_csv(cfg.STORAGE_ROOT_PATH + rf'\results_all_gcn_node.csv', sep=';')