diff options
| -rwxr-xr-x | server.py | 29 |
1 files changed, 14 insertions, 15 deletions
@@ -25,20 +25,19 @@ from nltk.stem.porter import PorterStemmer from collections import Counter import numpy as np from numpy import array +import tensorflow import keras -from keras.models import Model -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences -from keras.models import Sequential -from keras.layers import Dense -from keras.layers import Flatten -from keras.layers import Embedding -from keras.layers.convolutional import Conv1D -from keras.layers.convolutional import MaxPooling1D -from keras import metrics -from keras import optimizers +from tensorflow.keras.models import Model +from tensorflow.keras.preprocessing.text import Tokenizer +from tensorflow.keras.preprocessing.sequence import pad_sequences +from tensorflow.keras.layers import * +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.layers import Flatten +from tensorflow.keras.layers import Embedding +from tensorflow.keras import metrics +from tensorflow.keras import optimizers import pickle -import tensorflow as tf app=Flask(__name__) datadir="/export/ratspub/" @@ -84,8 +83,8 @@ def create_model(vocab_size, max_length): model.add(Flatten()) model.add(Dense(10, activation='relu')) model.add(Dense(1, activation='sigmoid')) - opt = keras.optimizers.Adamax(learning_rate=0.002, beta_1=0.9, beta_2=0.999) - model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[keras.metrics.AUC()]) + opt = tensorflow.keras.optimizers.Adamax(learning_rate=0.002, beta_1=0.9, beta_2=0.999) + model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[tensorflow.keras.metrics.AUC()]) return model @app.route("/") @@ -602,7 +601,7 @@ def sentences(): line = ' '.join(tokens) line = [line] tokenized_sent = tokenizer.texts_to_sequences(line) - tokenized_sent = pad_sequences(tokenized_sent, maxlen=max_length, padding='post') + tokenized_sent = pad_sequences(tokenized_sent, maxlen=max_length, padding='post') predict_sent = model.predict(tokenized_sent, verbose=0) percent_sent = predict_sent[0,0] if round(percent_sent) == 0: |