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authorbloodstalker <thabogre@gmail.com>2018-09-03 16:07:04 +0000
committerbloodstalker <thabogre@gmail.com>2018-09-03 16:07:04 +0000
commit0fbff8a7df8fdee5fae3cc33d03f0401defcb579 (patch)
tree4880ae2cb976de5757ecefe6922b817bc14fb305 /cnn.py
parentupdate (diff)
downloadseer-0fbff8a7df8fdee5fae3cc33d03f0401defcb579.tar.gz
seer-0fbff8a7df8fdee5fae3cc33d03f0401defcb579.zip
update
Diffstat (limited to 'cnn.py')
-rwxr-xr-xcnn.py98
1 files changed, 95 insertions, 3 deletions
diff --git a/cnn.py b/cnn.py
index 8e8eb98..7cb1493 100755
--- a/cnn.py
+++ b/cnn.py
@@ -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()