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[Bug] Fix Stateful Metrics in fit_generator with TensorBoard #10673

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merged 3 commits into from
Jul 23, 2018
Merged

[Bug] Fix Stateful Metrics in fit_generator with TensorBoard #10673

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ae966f2
Fix casting validate generator with stateful metrics.
briannemsick Jul 13, 2018
2e02247
Add a stateful metric to Tensboard tests
briannemsick Jul 16, 2018
6cc0ca8
Merge branch 'master' into master
briannemsick Jul 19, 2018
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2 changes: 1 addition & 1 deletion keras/engine/training_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -375,7 +375,7 @@ def evaluate_generator(model, generator,
averages.append(np.average([out[i] for out in outs_per_batch],
weights=batch_sizes))
else:
averages.append(float(outs_per_batch[-1][i]))
averages.append(np.float64(outs_per_batch[-1][i]))
return unpack_singleton(averages)


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17 changes: 15 additions & 2 deletions tests/keras/test_callbacks.py
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Expand Up @@ -10,7 +10,7 @@
from keras import initializers
from keras import callbacks
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Dropout, add, dot, Lambda
from keras.layers import Input, Dense, Dropout, add, dot, Lambda, Layer
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D, GlobalAveragePooling1D, GlobalAveragePooling2D
from keras.utils.test_utils import get_test_data
Expand Down Expand Up @@ -500,14 +500,27 @@ def data_generator(train):
i += 1
i = i % max_batch_index

class DummyStatefulMetric(Layer):

def __init__(self, name='dummy_stateful_metric', **kwargs):
super(DummyStatefulMetric, self).__init__(name=name, **kwargs)
self.stateful = True
self.state = K.variable(value=0, dtype='int32')

def reset_states(self):
pass

def __call__(self, y_true, y_pred):
return self.state

inp = Input((input_dim,))
hidden = Dense(num_hidden, activation='relu')(inp)
hidden = Dropout(0.1)(hidden)
output = Dense(num_classes, activation='softmax')(hidden)
model = Model(inputs=inp, outputs=output)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
metrics=['accuracy', DummyStatefulMetric()])

# we must generate new callbacks for each test, as they aren't stateless
def callbacks_factory(histogram_freq, embeddings_freq=1):
Expand Down