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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Keras metrics functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python import keras
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.platform import test
class KerasMetricsTest(test.TestCase):
def test_metrics(self):
with self.test_session():
y_a = keras.backend.variable(np.random.random((6, 7)))
y_b = keras.backend.variable(np.random.random((6, 7)))
for metric in [keras.metrics.binary_accuracy,
keras.metrics.categorical_accuracy]:
output = metric(y_a, y_b)
self.assertEqual(keras.backend.eval(output).shape, (6,))
def test_sparse_categorical_accuracy(self):
with self.test_session():
metric = keras.metrics.sparse_categorical_accuracy
y_a = keras.backend.variable(np.random.randint(0, 7, (6,)))
y_b = keras.backend.variable(np.random.random((6, 7)))
self.assertEqual(keras.backend.eval(metric(y_a, y_b)).shape, (6,))
def test_sparse_top_k_categorical_accuracy(self):
with self.test_session():
y_pred = keras.backend.variable(np.array([[0.3, 0.2, 0.1],
[0.1, 0.2, 0.7]]))
y_true = keras.backend.variable(np.array([[1], [0]]))
result = keras.backend.eval(
keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=3))
self.assertEqual(result, 1)
result = keras.backend.eval(
keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=2))
self.assertEqual(result, 0.5)
result = keras.backend.eval(
keras.metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=1))
self.assertEqual(result, 0.)
def test_top_k_categorical_accuracy(self):
with self.test_session():
y_pred = keras.backend.variable(np.array([[0.3, 0.2, 0.1],
[0.1, 0.2, 0.7]]))
y_true = keras.backend.variable(np.array([[0, 1, 0], [1, 0, 0]]))
result = keras.backend.eval(
keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=3))
self.assertEqual(result, 1)
result = keras.backend.eval(
keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=2))
self.assertEqual(result, 0.5)
result = keras.backend.eval(
keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=1))
self.assertEqual(result, 0.)
def test_stateful_metrics(self):
with self.test_session():
np.random.seed(1334)
class BinaryTruePositives(keras.layers.Layer):
"""Stateful Metric to count the total true positives over all batches.
Assumes predictions and targets of shape `(samples, 1)`.
Arguments:
threshold: Float, lower limit on prediction value that counts as a
positive class prediction.
name: String, name for the metric.
"""
def __init__(self, name='true_positives', **kwargs):
super(BinaryTruePositives, self).__init__(name=name, **kwargs)
self.true_positives = keras.backend.variable(value=0, dtype='int32')
self.stateful = True
def reset_states(self):
keras.backend.set_value(self.true_positives, 0)
def __call__(self, y_true, y_pred):
"""Computes the number of true positives in a batch.
Args:
y_true: Tensor, batch_wise labels
y_pred: Tensor, batch_wise predictions
Returns:
The total number of true positives seen this epoch at the
completion of the batch.
"""
y_true = math_ops.cast(y_true, 'int32')
y_pred = math_ops.cast(math_ops.round(y_pred), 'int32')
correct_preds = math_ops.cast(math_ops.equal(y_pred, y_true), 'int32')
true_pos = math_ops.cast(
math_ops.reduce_sum(correct_preds * y_true), 'int32')
current_true_pos = self.true_positives * 1
self.add_update(
state_ops.assign_add(self.true_positives, true_pos),
inputs=[y_true, y_pred])
return current_true_pos + true_pos
metric_fn = BinaryTruePositives()
config = keras.metrics.serialize(metric_fn)
metric_fn = keras.metrics.deserialize(
config, custom_objects={'BinaryTruePositives': BinaryTruePositives})
# Test on simple model
inputs = keras.Input(shape=(2,))
outputs = keras.layers.Dense(1, activation='sigmoid')(inputs)
model = keras.Model(inputs, outputs)
model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=['acc', metric_fn])
# Test fit, evaluate
samples = 100
x = np.random.random((samples, 2))
y = np.random.randint(2, size=(samples, 1))
val_samples = 10
val_x = np.random.random((val_samples, 2))
val_y = np.random.randint(2, size=(val_samples, 1))
history = model.fit(x, y,
epochs=1,
batch_size=10,
validation_data=(val_x, val_y))
outs = model.evaluate(x, y, batch_size=10)
preds = model.predict(x)
def ref_true_pos(y_true, y_pred):
return np.sum(np.logical_and(y_pred > 0.5, y_true == 1))
# Test correctness (e.g. updates should have been run)
self.assertAllClose(outs[2], ref_true_pos(y, preds), atol=1e-5)
# Test correctness of the validation metric computation
val_preds = model.predict(val_x)
val_outs = model.evaluate(val_x, val_y, batch_size=10)
self.assertAllClose(
val_outs[2], ref_true_pos(val_y, val_preds), atol=1e-5)
self.assertAllClose(
val_outs[2], history.history['val_true_positives'][-1], atol=1e-5)
# Test with generators
gen = [(np.array([x0]), np.array([y0])) for x0, y0 in zip(x, y)]
val_gen = [(np.array([x0]), np.array([y0]))
for x0, y0 in zip(val_x, val_y)]
history = model.fit_generator(iter(gen),
epochs=1,
steps_per_epoch=samples,
validation_data=iter(val_gen),
validation_steps=val_samples)
outs = model.evaluate_generator(iter(gen), steps=samples)
preds = model.predict_generator(iter(gen), steps=samples)
# Test correctness of the metric results
self.assertAllClose(outs[2], ref_true_pos(y, preds), atol=1e-5)
# Test correctness of the validation metric computation
val_preds = model.predict_generator(iter(val_gen), steps=val_samples)
val_outs = model.evaluate_generator(iter(val_gen), steps=val_samples)
self.assertAllClose(
val_outs[2], ref_true_pos(val_y, val_preds), atol=1e-5)
self.assertAllClose(
val_outs[2], history.history['val_true_positives'][-1], atol=1e-5)
if __name__ == '__main__':
test.main()
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