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Updated for Tensorflow-gpu 2.1 #4

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Updated for Tensorflow-gpu 2.1 #4

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32 changes: 26 additions & 6 deletions codes/model_baseline/model_baseline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,19 @@
import random
import numpy as np
import tensorflow as tf
tf.set_random_seed(0)
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)

# tf.set_random_seed(0)
## modification
tf.random.set_seed(0) ## For tf-2.1
## modification

# session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
##modification
## Allowing growth will help removing junction while on a remote server.
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU
config.log_device_placement = True # to log device placement (on which device the operation ran)
## modification

from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras.models import Sequential, Model
Expand Down Expand Up @@ -60,8 +71,12 @@ def model(x_train, num_labels, LSTM_units, dropout, num_conv_filters, batch_size
os.environ["CUDA_DEVICE_ORDER"]= "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]= '1'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)

# sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
# K.set_session(sess)
##Updating for tf-2.1
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=config)
tf.compat.v1.keras.backend.set_session(sess)

EPOCH = 10
BATCH_SIZE = 16
Expand Down Expand Up @@ -138,7 +153,10 @@ def model(x_train, num_labels, LSTM_units, dropout, num_conv_filters, batch_size
rnn_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])

history = rnn_model.fit(X_train_, y_train_one_hot, epochs=EPOCH, batch_size=BATCH_SIZE, verbose=1, callbacks=callbacks, validation_data=(X_test_, y_test_one_hot))

##Need to save weights using this:
rnn_model.save_weights('my_model_weights.h5')
## Since conventional code didnt work for me.

early_stopping_epoch = callbacks[1].stopped_epoch - PATIENCE + 1
print('Early stopping epoch: ' + str(early_stopping_epoch))
early_stopping_epoch_list.append(early_stopping_epoch)
Expand All @@ -148,7 +166,9 @@ def model(x_train, num_labels, LSTM_units, dropout, num_conv_filters, batch_size

# Evaluate model and predict data on TEST
print("******Evaluating TEST set*********")
rnn_model.load_weights(model_filename)
## Loading weights:
rnn_model.load_weights('my_model_weights.h5')

y_test_predict = rnn_model.predict(X_test_, batch_size = BATCH_SIZE)
y_test_predict = np.array(y_test_predict)
y_test_predict = np.argmax(y_test_predict, axis=1)
Expand Down