import os
wandb.init()
config = wandb.config
# set parameters:
config.vocab_size = 1000
config.maxlen = 300
config.batch_size = 32
config.embedding_dims = 50
config.filters = 250
config.kernel_size = 3
config.hidden_dims = 100
config.epochs = 10
(X_train, y_train), (X_test, y_test) = imdb.load_imdb()
if not os.path.exists("glove.6B.100d.txt"):
print("Downloading glove embeddings...")
subprocess.check_output(
"curl -OL http://nlp.stanford.edu/data/glove.6B.zip && unzip glove.6B.zip",
shell=True)
print("Tokenizing input...")
tokenizer = text.Tokenizer(num_words=config.vocab_size)
tokenizer.fit_on_texts(X_train)
X_train = tokenizer.texts_to_sequences(X_train)
X_test = tokenizer.texts_to_sequences(X_test)
X_train = sequence.pad_sequences(X_train, maxlen=config.maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=config.maxlen)
from keras.preprocessing import text
wandb.init()
config = wandb.config
# set parameters:
config.vocab_size = 1000
config.maxlen = 1000
config.batch_size = 32
config.embedding_dims = 50
config.filters = 250
config.kernel_size = 3
config.hidden_dims = 250
config.epochs = 10
(X_train, y_train), (X_test, y_test) = imdb.load_imdb()
print("Review", X_train[0])
print("Label", y_train[0])
tokenizer = text.Tokenizer(num_words=config.vocab_size)
tokenizer.fit_on_texts(X_train)
X_train = tokenizer.texts_to_sequences(X_train)
X_test = tokenizer.texts_to_sequences(X_test)
X_train = sequence.pad_sequences(X_train, maxlen=config.maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=config.maxlen)
print(X_train.shape)
print("After pre-processing", X_train[0])
model = Sequential()
model.add(Embedding(config.vocab_size,
