Print test cost for network.py
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@@ -1,3 +1,2 @@
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[TYPECHECK]
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ignored-modules = numpy, numpy.random
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ignored-classes = numpy
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ignored-modules = numpy, numpy.random
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@@ -241,6 +241,8 @@ PATIENCE = 200
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print TRAINING_SUBSET_SIZE
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print "Epoch\tTraining Cost Function\tTest Cost Function"
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best_rate = np.inf
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best_model = None
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for epoch in range(MAX_EPOCHS):
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@@ -263,11 +265,11 @@ for epoch in range(MAX_EPOCHS):
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batch = training_subset[i:min(i + BATCH_SIZE, len(training_subset))]
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MODEL.backward_minibatch(batch, LEARNING_RATE)
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# Evaluate accuracy against training data
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# Evaluate accuracy against training data and test data
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training_rate = evaluate(MODEL, training_subset)
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# test_rate = evaluate(MODEL, TEST_DATA)
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test_rate = evaluate(MODEL, TEST_DATA)
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print epoch, "training:", training_rate,
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print epoch, training_rate, test_rate,
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# If it's the best one so far, store it
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if training_rate < best_rate:
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