Amazon Sentiment Data



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%load_ext autoreload
%autoreload 2



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import lxmls.readers.sentiment_reader as srs
from lxmls.deep_learning.utils import AmazonData
corpus = srs.SentimentCorpus("books")
data = AmazonData(corpus=corpus)

Implement Pytorch Forward pass

As the final exercise today implement the log forward() method in

lxmls/deep_learning/pytorch_models/mlp.py

Use the previous exercise as reference. After you have completed this you can run both systems for comparison.



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# Model
geometry = [corpus.nr_features, 20, 2]
activation_functions = ['sigmoid', 'softmax']

# Optimization
learning_rate = 0.05
num_epochs = 10
batch_size = 30



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import numpy as np
from lxmls.deep_learning.pytorch_models.mlp import PytorchMLP
model = PytorchMLP(
    geometry=geometry,
    activation_functions=activation_functions,
    learning_rate=learning_rate
)



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# Get batch iterators for train and test
train_batches = data.batches('train', batch_size=batch_size)
test_set = data.batches('test', batch_size=None)[0]

# Epoch loop
for epoch in range(num_epochs):

    # Batch loop
    for batch in train_batches:
        model.update(input=batch['input'], output=batch['output'])

    # Prediction for this epoch
    hat_y = model.predict(input=test_set['input'])

    # Evaluation
    accuracy = 100*np.mean(hat_y == test_set['output'])

    # Inform user
    print("Epoch %d: accuracy %2.2f %%" % (epoch+1, accuracy))