diff options
Diffstat (limited to '')
| -rwxr-xr-x | cnn.py | 98 |
1 files changed, 95 insertions, 3 deletions
@@ -20,10 +20,19 @@ from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten, Reshape from keras.layers import Conv1D, MaxPooling1D, LeakyReLU, PReLU from keras.layers import LSTM +from keras.layers import GRU, CuDNNGRU from keras.utils import np_utils from keras.callbacks import CSVLogger, ModelCheckpoint import tensorflow as tf from keras.backend.tensorflow_backend import set_session +from keras import applications +from keras.models import Model +from scipy.ndimage import imread +import random +from keras import backend as K +import keras +from keras import optimizers +import matplotlib.pyplot as plt def SigHandler_SIGINT(signum, frame): print() @@ -164,12 +173,14 @@ def cnn_type_1(symbol_str): model.compile(loss='mse', optimizer='adam') model.fit(training_datas, training_labels,verbose=1, batch_size=batch_size,validation_data=(validation_datas,validation_labels), epochs = epochs, callbacks=[CSVLogger(output_file_name+'.csv', append=True),ModelCheckpoint(output_file_name+'-{epoch:02d}-{val_loss:.5f}.hdf5', monitor='val_loss', verbose=1,mode='min')]) -def lstm_type_cnn_1(symbol_str): +def lstm_type_cnn_1(symbol_str, kind): df, original_df, time_stamps = getData(symbol_str) Scaler(df, original_df, time_stamps, symbol_str) + """ os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = '1' os.environ['TF_CPP_MIN_LOG_LEVEL']='2' + """ config = tf.ConfigProto() config.gpu_options.allow_growth = True @@ -196,21 +207,102 @@ def lstm_type_cnn_1(symbol_str): #build model model = Sequential() - model.add(LSTM(units=units,activation='tanh', input_shape=(step_size,nb_features),return_sequences=False)) + if kind == "GRU": + model.add(GRU(units=units,activation='tanh', input_shape=(step_size,nb_features),return_sequences=False)) + elif kind == "LSTM": + model.add(LSTM(units=units,activation='tanh', input_shape=(step_size,nb_features),return_sequences=False)) model.add(Dropout(0.8)) model.add(Dense(output_size)) model.add(LeakyReLU()) model.compile(loss='mse', optimizer='adam') model.fit(training_datas, training_labels, batch_size=batch_size,validation_data=(validation_datas,validation_labels), epochs = epochs, callbacks=[CSVLogger(output_file_name+'.csv', append=True),ModelCheckpoint(output_file_name+'-{epoch:02d}-{val_loss:.5f}.hdf5', monitor='val_loss', verbose=1,mode='min')]) +def load_cnn_type_1(symbol_str): + df, original_df, time_stamps = getData(symbol_str) + Scaler(df, original_df, time_stamps, symbol_str) + """ + os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' + os.environ['CUDA_VISIBLE_DEVICES'] = '0' + os.environ['TF_CPP_MIN_LOG_LEVEL']='2' + """ + + with h5py.File("".join("./cnn/" + symbol_str + "_close.h5"), "r") as hf: + datas = hf['inputs'].value + labels = hf['outputs'].value + input_times = hf['input_times'].value + output_times = hf['output_times'].value + original_inputs = hf['original_inputs'].value + original_outputs = hf['original_outputs'].value + original_datas = hf['original_datas'].value + + scaler=MinMaxScaler() + #split training validation + training_size = int(0.8* datas.shape[0]) + training_datas = datas[:training_size,:,:] + training_labels = labels[:training_size,:,:] + validation_datas = datas[training_size:,:,:] + validation_labels = labels[training_size:,:,:] + validation_original_outputs = original_outputs[training_size:,:,:] + validation_original_inputs = original_inputs[training_size:,:,:] + validation_input_times = input_times[training_size:,:,:] + validation_output_times = output_times[training_size:,:,:] + + ground_true = np.append(validation_original_inputs,validation_original_outputs, axis=1) + ground_true_times = np.append(validation_input_times,validation_output_times, axis=1) + step_size = datas.shape[1] + batch_size= 8 + nb_features = datas.shape[2] + + model = Sequential() + + # 2 layers + model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=20)) + # model.add(LeakyReLU()) + model.add(Dropout(0.25)) + model.add(Conv1D( strides=4, filters=nb_features, kernel_size=16)) + model.load_weights("cnn/" + symbol_str + "_CNN_2_relu-76-0.00056.hdf5") + model.compile(loss='mse', optimizer='adam') + + predicted = model.predict(validation_datas) + predicted_inverted = [] + + for i in range(original_datas.shape[1]): + scaler.fit(original_datas[:, i].reshape(-1, 1)) + predicted_inverted.append(scaler.inverse_transform(predicted[:,:,i])) + print(np.array(predicted_inverted).shape) + #get only the close data + ground_true = ground_true[:,:,0].reshape(-1) + ground_true_times = ground_true_times.reshape(-1) + ground_true_times = pd.to_datetime(ground_true_times, unit='s') + # since we are appending in the first dimension + predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1) + print(np.array(predicted_inverted).shape) + validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s') + + ground_true_df = pd.DataFrame() + ground_true_df['times'] = ground_true_times + ground_true_df['value'] = ground_true + + prediction_df = pd.DataFrame() + prediction_df['times'] = validation_output_times + prediction_df['value'] = predicted_inverted + + prediction_df = prediction_df.loc[(prediction_df["times"].dt.year == 2017 )&(prediction_df["times"].dt.month > 7 ),: ] + ground_true_df = ground_true_df.loc[(ground_true_df["times"].dt.year == 2017 )&(ground_true_df["times"].dt.month > 7 ),:] + plt.figure(figsize=(20,10)) + plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual') + plt.plot(prediction_df.times,prediction_df.value,'ro', label='Predicted') + plt.legend(loc='upper left') + plt.show() # write code here def premain(argparser): signal.signal(signal.SIGINT, SigHandler_SIGINT) #here #cnn_type_1("ETH") - lstm_type_cnn_1("ETH") + #lstm_type_cnn_1("ETH", "GRU") + load_cnn_type_1("ETH") def main(): argparser = Argparser() |