/
parallel_search.py
137 lines (94 loc) · 4.77 KB
/
parallel_search.py
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import pickle
import time
import tensorboard
from keras.callbacks import TensorBoard, EarlyStopping
import numpy as np
import sys
np.set_printoptions ( threshold=sys.maxsize )
# from keras.callbacks import TensorBoard
from keras.layers import Input, Dense, Dropout
from keras.layers import LSTM, CuDNNLSTM
from keras.models import Model
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
name = "42_-{}".format ( int ( time.time () ) )
tensorboard = TensorBoard ( log_dir="logs42/{}".format ( name ), write_graph=True, write_grads=False,
write_images=False, update_freq='epoch' )
es = EarlyStopping ( monitor='val_loss', mode='min', verbose=1 )
with open ( '42_features', 'rb' ) as fp:
X = pickle.load ( fp )
with open ( '42_labels', 'rb' ) as fp:
y = pickle.load ( fp )
X = np.expand_dims ( X, axis=2 )
newy = []
count = 0
for val in y:
if val == 0:
count += 1
newy.append ( np.array ( [1, 0] ) )
else:
newy.append ( np.array ( [0, 1] ) )
y = np.array ( newy )
print ( count )
X_train, X_test, y_train, y_test = train_test_split ( X, y, test_size=0.3 )
input = Input ( shape=(42, 1) )
fi = 0
arr = [] ## to save the trials
import csv
csvfile = "optimization_oneLSTM_oneDense.csv"
with open ( csvfile, "w" ) as csvoutput:
writer = csv.writer ( csvoutput, lineterminator='\n' )
writer.writerow ( ["Trial number", "LSTM size", "Dense size", "Dropout Ratio", "Batch Size", "Activation Function",
"Number of Epoch", "Accuracy", "Best Val_Loss"] )
# fi+= 1
dense_layer_sizes = [64, 128, 256, 512]
layer_sizes = [64, 128, 256]
n_dropout = [0, 0.2, 0.4, 0.6]
batch_s = [16, 32, 64, 128]
input = Input ( shape=(42, 1) )
print ( input.shape )
for i in range ( 0, len ( layer_sizes ) ):
for s in range ( 0, len ( dense_layer_sizes ) ):
for dro in range ( 0, len ( n_dropout ) ):
for bs in range ( len ( batch_s ) ):
NAME = "{}-1st-LSTM-{}-1st-dense-dropout-{}-batch-{}-FI{}".format ( layer_sizes[i],
dense_layer_sizes[s],
n_dropout[dro], batch_s[bs], fi )
tensorboard = TensorBoard ( log_dir="logs/{}".format ( NAME ) )
lstm_model = CuDNNLSTM ( layer_sizes[i], return_sequences=True ) ( input )
lstm_model = CuDNNLSTM ( layer_sizes[i] * 2 ) ( input )
if (layer_sizes[i]) == s:
d = Dense ( dense_layer_sizes[s], activation='relu' ) ( lstm_model )
d = Dropout ( n_dropout[dro] ) ( d )
d = Dense ( dense_layer_sizes[s] * 2, activation='relu' ) ( d )
d = Dropout ( n_dropout[dro] ) ( d )
d = Dense ( dense_layer_sizes[s] * 4, activation='relu' ) ( d )
d = Dropout ( n_dropout[dro] ) ( d )
d = Dense ( dense_layer_sizes[s] * 8, activation='relu' ) ( d )
d = Dropout ( n_dropout[dro] ) ( d )
else:
d = Dense ( dense_layer_sizes[i] * 2, activation='relu' ) ( lstm_model )
d = Dropout ( n_dropout[dro] ) ( d )
d = Dense ( dense_layer_sizes[i] * 4, activation='relu' ) ( d )
d = Dropout ( n_dropout[dro] ) ( d )
d = Dense ( dense_layer_sizes[i] * 8, activation='relu' ) ( d )
d = Dropout ( n_dropout[dro] ) ( d )
d = Dense ( dense_layer_sizes[i] * 16, activation='relu' ) ( d )
d = Dropout ( n_dropout[dro] ) ( d )
output = Dense ( 2, activation='softmax' ) ( d )
model = Model ( inputs=input, outputs=output )
adam = Adam ( lr=0.0001 )
model.compile ( optimizer=adam, loss='binary_crossentropy', metrics=['acc'] )
model.fit ( X_train, y_train, batch_size=batch_s[bs], epochs=100, validation_data=(X_test, y_test),
callbacks=[tensorboard, es] )
model.save ( '{}.h5'.format ( NAME ) )
when = es.stopped_epoch
bes = es.best
fi += 1
arr.append (
[fi, layer_sizes[i], dense_layer_sizes[s], n_dropout[dro], batch_s[bs], 'relu', when, bes] )
with open ( csvfile, "a",
newline='' ) as csvoutput: # 'a' parameter allows you to append to the end of the file instead of simply overwriting the existing content
# with open(csvfile, "w") as csvoutput:
writer = csv.writer ( csvoutput )
writer.writerows ( arr )