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Enable accuracy reporting during training in examples/mnist_siamese_graph.py #7997

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Merged
merged 2 commits into from
Oct 2, 2017
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Enable accuracy reporting during training in examples/mnist_siamese_graph.py #7997

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f12f2bd
1. Enable accuracy reporting during training.
ozabluda Sep 26, 2017
19abcbf
Fix PEP8 errors
ozabluda Sep 26, 2017
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11 changes: 8 additions & 3 deletions examples/mnist_siamese_graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,8 +76,13 @@ def compute_accuracy(predictions, labels):
'''Compute classification accuracy with a fixed threshold on distances.
'''
preds = predictions.ravel() < 0.5
return ((preds & labels).sum() +
(np.logical_not(preds) & np.logical_not(labels)).sum()) / float(labels.size)
return np.mean(preds == labels)


def accuracy(y_true, y_pred):
'''Compute classification accuracy with a fixed threshold on distances.
'''
return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))


# the data, shuffled and split between train and test sets
Expand Down Expand Up @@ -117,7 +122,7 @@ def compute_accuracy(predictions, labels):

# train
rms = RMSprop()
model.compile(loss=contrastive_loss, optimizer=rms)
model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
batch_size=128,
epochs=epochs,
Expand Down