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Add a tracking of global loss in each epoch #23

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Add a tracking of global loss in each epoch #23

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a5c1038
Add global loss tracker
ducovrossem Dec 19, 2014
6bce0da
Add global loss computation
ducovrossem Dec 19, 2014
af11285
Verbose dependency and return fit_vectors
ducovrossem Dec 19, 2014
f6c4f9f
fit_vectors returns global_loss
ducovrossem Dec 19, 2014
0ac1497
Indentation level in fit_vector function
ducovrossem Jan 2, 2015
654eb4f
Calculate weightless loss seperately
ducovrossem Jan 2, 2015
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31 changes: 18 additions & 13 deletions glove/glove.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,8 @@ def __init__(self, no_components=30, learning_rate=0.05,

self.dictionary = None
self.inverse_dictionary = None

self.global_loss = None

def fit(self, matrix, epochs=5, no_threads=2, verbose=False):
"""
Expand Down Expand Up @@ -86,19 +88,22 @@ def fit(self, matrix, epochs=5, no_threads=2, verbose=False):

# Shuffle the coocurrence matrix
np.random.shuffle(shuffle_indices)

fit_vectors(self.word_vectors,
self.vectors_sum_gradients,
self.word_biases,
self.biases_sum_gradients,
matrix.row,
matrix.col,
matrix.data,
shuffle_indices,
self.learning_rate,
self.max_count,
self.alpha,
int(no_threads))

self.global_loss = fit_vectors(self.word_vectors,
self.vectors_sum_gradients,
self.word_biases,
self.biases_sum_gradients,
matrix.row,
matrix.col,
matrix.data,
shuffle_indices,
self.learning_rate,
self.max_count,
self.alpha,
int(no_threads))

if verbose:
print('Global loss: %d' % self.global_loss)

if not np.isfinite(self.word_vectors).all():
raise Exception('Non-finite values in word vectors. '
Expand Down
13 changes: 11 additions & 2 deletions glove/glove_cython.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,9 @@ def fit_vectors(double[:, ::1] wordvec,

# Loss and gradient variables.
cdef double prediction, entry_weight, loss

# Define global loss
cdef double global_loss

# Iteration variables
cdef int i, j, shuffle_index
Expand All @@ -74,7 +77,12 @@ def fit_vectors(double[:, ::1] wordvec,

# Compute loss and the example weight.
entry_weight = double_min(1.0, (count / max_count)) ** alpha
loss = entry_weight * (prediction - c_log(count))

loss_unweighted = prediction - c_log(count)
loss = entry_weight * loss_unweighted

# Update the weighted global loss
global_loss += 0.5 * loss * loss_unweighted

# Update step: apply gradients and reproject
# onto the unit sphere.
Expand All @@ -100,7 +108,8 @@ def fit_vectors(double[:, ::1] wordvec,
learning_rate = initial_learning_rate / sqrt(wordbias_sum_gradients[word_b])
wordbias[word_b] -= learning_rate * loss
wordbias_sum_gradients[word_b] += loss ** 2


return global_loss

def transform_paragraph(double[:, ::1] wordvec,
double[::1] wordbias,
Expand Down