[Kaggle DAY07]Real or Not? NLP with Disaster Tweets!

Kaggle 도전 7회차!

오늘은 CNN 모델을 사용해보기로 했습니다.

 

첫번째 제출

model = Sequential()
model.add(Embedding(max_words, 128, input_length=23))
model.add(Dropout(0.2))
model.add(Conv1D(256,
                 3,
                 padding='valid',
                 activation='relu',
                 strides=1))
model.add(GlobalMaxPooling1D())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(2, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) 
history = model.fit(X_train_vec, y_train, epochs=3, batch_size=32, validation_split=0.1)

결과

 

두번째 제출

model2 = Sequential()
model2.add(Embedding(max_words, 128, input_length=23))
model2.add(Dropout(0.2))
model2.add(Conv1D(256,
                 3,
                 padding='valid',
                 activation='relu',
                 strides=1))
model2.add(GlobalMaxPooling1D())
model2.add(Dense(128, activation='relu'))
model2.add(Dropout(0.2))
model2.add(Dense(2, activation='sigmoid'))
model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) 
history = model2.fit(X_train_vec, y_train, epochs=1, batch_size=32, validation_split=0.1)

결과

 

세번째 제출

model2 = Sequential()
model2.add(Embedding(max_words, 128, input_length=23))
model2.add(Dropout(0.2))
model2.add(Conv1D(256,
                 3,
                 padding='valid',
                 activation='relu',
                 strides=1))
model2.add(GlobalMaxPooling1D())
model2.add(Dense(64, activation='relu'))
model2.add(Dropout(0.2))
model2.add(Dense(2, activation='sigmoid'))
model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) 
history2 = model2.fit(X_train_vec, y_train, epochs=1, batch_size=32, validation_split=0.1)

결과

 

네번째 제출

model2 = Sequential()
model2.add(Embedding(max_words, 128, input_length=23))
model2.add(Dropout(0.2))
model2.add(Conv1D(256,
                 3,
                 padding='valid',
                 activation='relu',
                 strides=1))
model2.add(GlobalMaxPooling1D())
model2.add(Dense(32, activation='relu'))
model2.add(Dropout(0.2))
model2.add(Dense(2, activation='sigmoid'))
model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) 
history2 = model2.fit(X_train_vec, y_train, epochs=1, batch_size=32, validation_split=0.1)

결과

 

다섯번째 제출

model2 = Sequential()
model2.add(Embedding(max_words, 128, input_length=23))
model2.add(Dropout(0.2))
model2.add(Conv1D(256,
                 3,
                 padding='valid',
                 activation='relu',
                 strides=1))
model2.add(GlobalMaxPooling1D())
model2.add(Dense(32, activation='relu'))
model2.add(Dropout(0.2))
model2.add(Dense(2, activation='sigmoid'))
model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) 
history2 = model2.fit(X_train_vec, y_train, epochs=1, batch_size=16, validation_split=0.1)

결과