Devanshchowdhury2212

model = keras.models.load_model('LSTM.h5')
model.evaluate(X_train,y_train)
model.evaluate(X_val,y_val)

Devanshchowdhury2212

keras.backend.clear_session()
model = keras.models.Sequential()
model.add(keras.layers.Embedding(vocab_size, 32))
model.add(keras.layers.LSTM(3,recurrent_dropout=.4,dropout=.4,return_sequences=False,kernel_regularizer=keras.regularizers.l2()))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.summary()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
all_callbacks = [keras.callbacks.EarlyStopping(patience = 1,min_delta=.02),
                 keras.callbacks.ModelCheckpoint('LSTM.h5',save_best_only=True,monitor='val_accuracy')]
history  = model.fit(X_train,y_train,validation_data= (X_val,y_val),batch_size = 64,epochs=3,callbacks=all_callbacks)