diff options
Diffstat (limited to 'lstm.py')
| -rwxr-xr-x | lstm.py | 23 |
1 files changed, 17 insertions, 6 deletions
@@ -17,6 +17,7 @@ from keras.models import Sequential from keras.layers import Activation, Dense from keras.layers import LSTM from keras.layers import Dropout +from keras.models import load_model window_len = 10 split_date = "2017-06-01" @@ -75,7 +76,7 @@ def get_sets(crypto, model_data): LSTM_test_inputs = [np.array(LSTM_test_inputs) for LSTM_test_inputs in LSTM_test_inputs] LSTM_test_inputs = np.array(LSTM_test_inputs) - return LSTM_training_inputs, LSTM_test_inputs, training_set + return LSTM_training_inputs, LSTM_test_inputs, training_set, test_set def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, loss="mae", optimizer="adam"): model = Sequential() @@ -89,7 +90,7 @@ def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, def lstm_type_1(crypto, crypto_short): model_data = getData_CMC(crypto, crypto_short) np.random.seed(202) - training_inputs, test_inputs, training_set = get_sets(crypto, model_data) + training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data) model = build_model(training_inputs, output_size=1, neurons=20) training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1 history = model.fit(training_inputs, training_outputs, epochs=50, batch_size=1, verbose=2, shuffle=True) @@ -97,7 +98,7 @@ def lstm_type_1(crypto, crypto_short): def lstm_type_2(crypto, crypto_short, pred_range, neuron_count): model_data = getData_CMC(crypto, crypto_short) np.random.seed(202) - training_inputs, test_inputs, training_set = get_sets(crypto, model_data) + training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data) model = build_model(training_inputs, output_size=pred_range, neurons=neuron_count) training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1 training_outputs = [] @@ -109,7 +110,7 @@ def lstm_type_2(crypto, crypto_short, pred_range, neuron_count): def lstm_type_3(crypto, crypto_short, pred_range, neuron_count): model_data = getData_CMC(crypto, crypto_short) np.random.seed(202) - training_inputs, test_inputs, training_set = get_sets(crypto, model_data) + training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data) model = build_model(training_inputs, output_size=1, neurons=neuron_count) training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1 training_outputs = [] @@ -118,7 +119,16 @@ def lstm_type_3(crypto, crypto_short, pred_range, neuron_count): np.random.seed(rand_seed) temp_model = build_model(training_inputs, output_size=1, neurons=neuron_count) temp_model.fit(training_inputs, (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1, epochs=50, batch_size=1, verbose=0, shuffle=True) - temp_model.save(crypto + '_model_randseed_%d.h5'%rand_seed) + temp_model.save("./lstm-models/" + crypto + '_model_randseed_%d.h5'%rand_seed) + +def load_models(crypto, crypto_short): + preds = [] + model_data = getData_CMC(crypto, crypto_short) + np.random.seed(202) + training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data) + for rand_seed in range(775,800): + temp_model = load_model("./lstm-models/" + crypto + '_model_randseed_%d.h5'%rand_seed) + preds.append(np.mean(abs(np.transpose(temp_model.predict(test_inputs))-(test_set['Close'].values[window_len:]/test_set['Close'].values[:-window_len]-1)))) # write code here def premain(argparser): @@ -126,7 +136,8 @@ def premain(argparser): #here #lstm_type_1("ethereum", "ether") #lstm_type_2("ethereum", "ether", 5, 20) - lstm_type_3("ethereum", "ether", 5, 20) + #lstm_type_3("ethereum", "ether", 5, 20) + load_models("ethereum", "eth") def main(): argparser = Argparser() |