from keras.utils.vis_utils import plot_model
from model_def import deepmoji_architecture
model = deepmoji_architecture(nb_classes=63, nb_tokens=1000, maxlen=100)
plot_model(model,
to_file='model_architecture.png',
show_shapes=True,
show_layer_names=True)
test_label = [p[1] for p in item[1]]
train_X, _, _ = st.tokenize_sentences(train_text)
test_X, _, _ = st.tokenize_sentences(test_text)
train_y = np.array([label2index[l] for l in train_label])
test_y = np.array([label2index[l] for l in test_label])
nb_classes = len(label2index)
nb_tokens = len(vocabulary)
# use 20& of the training set for validation
train_X, val_X, train_y, val_y = train_test_split(train_X, train_y,
test_size=0.2, random_state=0)
# model
model = deepmoji_architecture(nb_classes=nb_classes,
nb_tokens=nb_tokens,
maxlen=MAX_LEN, embed_dropout_rate=0.25, final_dropout_rate=0.5, embed_l2=1E-6)
model.summary()
# load pretrained representation model
load_specific_weights(model, model_path, nb_tokens, MAX_LEN,
exclude_names=["softmax"])
# train model
model, acc = finetune(model, [train_X, val_X, test_X], [train_y, val_y, test_y], nb_classes, 100,
method="chain-thaw", verbose=2)
pred_y_prob = model.predict(test_X)
if nb_classes == 2:
pred_y = [0 if p < 0.5 else 1 for p in pred_y_prob]
# load vocabulary
with open(vocab_path, "r") as f_vocab:
vocabulary = json.load(f_vocab)
nb_tokens = len(vocabulary)
test_text = load_data(test_path)
# sentence tokenizer (MAXLEN means the max length of input text)
st = SentenceTokenizer(vocabulary, MAX_LEN)
# tokenize test text
test_X, _, _ = st.tokenize_sentences(test_text)
# load model
model = deepmoji_architecture(nb_classes=nb_classes,
nb_tokens=nb_tokens,
maxlen=MAX_LEN)
load_specific_weights(model,
model_path,
nb_tokens,
MAX_LEN,
nb_classes=nb_classes)
pred_y_prob = model.predict(test_X)
if nb_classes == 2:
pred_y = [0 if p < 0.5 else 1 for p in pred_y_prob]
else:
pred_y = np.argmax(pred_y_prob, axis=1)