Hey there everyone!
With the following model,
model = Sequential()
model.add(Input(shape=x_train.shape[1],))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(8, activation='softmax'))
model.compile(optimizer=Adam(learning_rate=0.01), loss=SparseCategoricalCrossentropy(), metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=200, epochs=50, validation_split=0.1)
I’ve got this classification report:
precision recall f1-score support
1 0.86 0.79 0.82 42275
2 0.83 0.91 0.87 56602
3 0.87 0.74 0.80 7269
4 0.81 0.60 0.69 546
5 0.82 0.30 0.44 1929
6 0.60 0.80 0.69 3496
7 0.87 0.77 0.82 4086
accuracy 0.84 116203
macro avg 0.81 0.70 0.73 116203
weighted avg 0.84 0.84 0.83 116203
I’ll have my presentation done later, but I’d like to point something out! It was the first time I’ve seen info on this Skill Path on how to save and open a model! Quite simple, just
model.save(‘path/to/save’)
and then open it with
model = keras.models.load_model(‘path/to/load’).
I would definitely consider having that not just a side note on the Final Project both on this path and also for the Data Science Path! The notion that models can be saved and utilized later on as a program is quite necessary! My bad if there is info on that during the paths 