tianjiashuo opened a new issue #20290:
URL: https://github.com/apache/incubator-mxnet/issues/20290
## Description
I am training a lstm-trec classification model. This is my training code.
```python
import os
#os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2"
os.environ["KERAS_BACKEND"] = 'mxnet'
import pandas as pd
import warnings
import re
import matplotlib.pyplot as plt
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
import numpy as np
np.random.seed(1234)
import keras.backend as K
print(f"Using {K.backend()} as backend")
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, LSTM, Embedding, Dropout, Conv1D,
MaxPooling1D, Bidirectional
from keras.models import Sequential
warnings.filterwarnings('ignore')
import keras
import numpy as np
max_features = 55
maxlen = 140
embed_size = 128
model_type = 'lstm'
save_dir = os.path.join(os.getcwd(), 'trec')
model_name = 'trec_%s_model_vgg.{epoch:03d}.h5' % model_type
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
filepath = os.path.join(save_dir, model_name)
from keras.callbacks import ModelCheckpoint
checkpoint =
ModelCheckpoint(filepath=filepath,monitor='val_accuracy',verbose=1,save_best_only=True)
callbacks = [checkpoint]
if os.path.exists('trec_test.h5'):
print("remove previous model weights")
os.remove('trec_test.h5')
import numpy as np
x_train=np.load('trec_train_x_char.npy')
y_train=np.load('trec_train_y_char.npy')
x_test=np.load('trec_test_x_char.npy')
y_test=np.load('trec_test_y_char.npy')
x_validation=np.load('trec_validation_x_char.npy')
y_validation=np.load('trec_validation_y_char.npy')
y_train = keras.utils.to_categorical(y_train)
y_validation = keras.utils.to_categorical(y_validation)
y_test = keras.utils.to_categorical(y_test)
#y_train = keras.utils.to_categorical(y_train)
#y_validation = keras.utils.to_categorical(y_validation)
#y_test = keras.utils.to_categorical(y_test)
def get_lstm_model(max_features, embed_size):
model = Sequential()
model.add(Embedding(max_features, embed_size))
model.add(Bidirectional(LSTM(128, recurrent_dropout=0.1)))
#model.add(LSTM(128, recurrent_dropout=0.1))
model.add(Dropout(0.25))
model.add(Dense(64))
model.add(Dropout(0.3))
model.add(Dense(6, activation='softmax'))
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['acc'])
return model
def model_fit(model, x, y, xv, yv):
return model.fit(x, y, batch_size=100, epochs=3,
validation_data=(xv,yv),callbacks=callbacks)
model = get_lstm_model(max_features, embed_size)
model_train = model_fit(model, x_train, y_train, x_validation, y_validation)
def model_predict(model, x):
return model.predict_classes(x)
score = model.evaluate(x_test,y_test)
model.save('trec_test.h5')
print('acc:',score[1])
print('after load')
load_model = keras.models.load_model('trec_test.h5')
load_model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['acc'])
score_1 = load_model.evaluate(x_test,y_test)
print(score_1[1])
```
### Error Message
```
Using MXNet backend
/usr/local/lib/python3.7/dist-packages/keras/__init__.py:31:
DeprecationWarning: MXNet support in Keras is going to be discontinued and
v2.2.4.3 is the last release as multi-backend Keras has been discontinued . It
is recommended to consider switching to MXNet Gluon. More information can be
found here: https://github.com/awslabs/keras-apache-mxnet
"https://github.com/awslabs/keras-apache-mxnet";, DeprecationWarning)
Using mxnet as backend
remove previous model weights
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, None, 128) 7040
_________________________________________________________________
bidirectional_1 (Bidirection (None, 256) 263168
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_1 (Dense) (None, 64) 16448
_________________________________________________________________
dropout_2 (Dropout) (None, 64) 0
_________________________________________________________________
dense_2 (Dense) (None, 6) 390
=================================================================
Total params: 287,046
Trainable params: 287,046
Non-trainable params: 0
_________________________________________________________________
Train on 5000 samples, validate on 452 samples
Epoch 1/3
5000/5000 [==============================] - 91s 18ms/step - loss: 1.6928 -
acc: 0.2158 - val_loss: 1.6423 - val_acc: 0.2478
Epoch 2/3
5000/5000 [==============================] - 88s 18ms/step - loss: 1.6555 -
acc: 0.2300 - val_loss: 1.6301 - val_acc: 0.2611
Epoch 3/3
5000/5000 [==============================] - 87s 17ms/step - loss: 1.6420 -
acc: 0.2418 - val_loss: 1.6300 - val_acc: 0.2544
500/500 [==============================] - 6s 12ms/step
acc: 0.3080000004768372
after load
500/500 [==============================] - 5s 9ms/step
0.1880000002384186
```
## To Reproduce
https://colab.research.google.com/drive/1J7Kd7cuBe1fnnx6p9TVOZ1_T93vI0kZx?usp=sharing#scrollTo=p_1SQCyjK-29
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