तंत्रिका नेटवर्क का उपयोग करके वाक्यांश संवेदना विश्लेषण

डेटा

प्रशिक्षण की तैयारी

def preprocessText(text):
    text = text.lower().replace("", "")
    text = re.sub('((www\.[^\s]+)|(https?://[^\s]+))', ' ', text)
    text = re.sub('@[^\s]+', ' ', text)
    text = re.sub('[^a-zA-Z--1-9]+', ' ', text)
    text = re.sub(' +', ' ', text)
    return text.strip()

उपकरण

हम अपना सुंदर कोड लिखते हैं

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential, load_model
model = tf.keras.Sequential()

model.add(
  tf.keras.layers.Dense(
    units=256,
    input_shape=(X_train.shape[1], ),
    activation='relu'
  )
)
model.add(
  tf.keras.layers.Dropout(rate=0.5)
)

model.add(
  tf.keras.layers.Dense(
    units=128,
    activation='relu'
  )
)
model.add(
  tf.keras.layers.Dropout(rate=0.5)
)

model.add(tf.keras.layers.Dense(2, activation='softmax'))
model.compile(
    loss='categorical_crossentropy',
    optimizer=tf.keras.optimizers.Adam(0.001),
    metrics=['accuracy']
)

history = model.fit(
    X_train, y_train,
    epochs=10,
    batch_size=16,
    validation_split=0.1,
    verbose=1,
    shuffle=True
)

model.evaluate(X_test, y_test)

Train on 53082 samples, validate on 5898 samples
Epoch 1/10
53082/53082 [==============================] - 12s 223us/sample - loss: 0.5451 - accuracy: 0.7207 - val_loss: 0.5105 - val_accuracy: 0.7397
Epoch 2/10
53082/53082 [==============================] - 11s 213us/sample - loss: 0.5129 - accuracy: 0.7452 - val_loss: 0.5000 - val_accuracy: 0.7523
Epoch 3/10
53082/53082 [==============================] - 11s 215us/sample - loss: 0.4885 - accuracy: 0.7624 - val_loss: 0.4914 - val_accuracy: 0.7538
Epoch 4/10
53082/53082 [==============================] - 11s 215us/sample - loss: 0.4686 - accuracy: 0.7739 - val_loss: 0.4865 - val_accuracy: 0.7589
Epoch 5/10
53082/53082 [==============================] - 11s 214us/sample - loss: 0.4474 - accuracy: 0.7889 - val_loss: 0.4873 - val_accuracy: 0.7616
Epoch 6/10
53082/53082 [==============================] - 11s 216us/sample - loss: 0.4272 - accuracy: 0.8004 - val_loss: 0.4878 - val_accuracy: 0.7603
Epoch 7/10
53082/53082 [==============================] - 11s 213us/sample - loss: 0.4081 - accuracy: 0.8111 - val_loss: 0.4986 - val_accuracy: 0.7594
Epoch 8/10
53082/53082 [==============================] - 11s 215us/sample - loss: 0.3899 - accuracy: 0.8241 - val_loss: 0.5101 - val_accuracy: 0.7564
Epoch 9/10
53082/53082 [==============================] - 11s 215us/sample - loss: 0.3733 - accuracy: 0.8315 - val_loss: 0.5035 - val_accuracy: 0.7633
Epoch 10/10
53082/53082 [==============================] - 11s 215us/sample - loss: 0.3596 - accuracy: 0.8400 - val_loss: 0.5239 - val_accuracy: 0.7620
6554/6554 [==============================] - 0s 53us/sample - loss: 0.5249 - accuracy: 0.7524
[0.5249265961105736, 0.752365]

परिणाम

निष्कर्ष