Here are the examples of the python api numpy.random.normal taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
145 Examples
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_correctness_2d():
model = Sequential()
norm = normalization.BatchNormalization(axis=1, input_shape=(10, 6), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='rmsprop')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 10, 6))
model.fit(x, x, epochs=5, verbose=0)
out = model.predict(x)
out -= np.reshape(K.eval(norm.beta), (1, 10, 1))
out /= np.reshape(K.eval(norm.gamma), (1, 10, 1))
astert_allclose(out.mean(axis=(0, 2)), 0.0, atol=1.1e-1)
astert_allclose(out.std(axis=(0, 2)), 1.0, atol=1.1e-1)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_correctness_1d():
model = Sequential()
norm = normalization.BatchNormalization(input_shape=(10,), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='rmsprop')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 10))
model.fit(x, x, epochs=5, verbose=0)
out = model.predict(x)
out -= K.eval(norm.beta)
out /= K.eval(norm.gamma)
astert_allclose(out.mean(), 0.0, atol=1e-1)
astert_allclose(out.std(), 1.0, atol=1e-1)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_correctness_1d():
model = Sequential()
norm = normalization.BatchNormalization(input_shape=(10,), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='rmsprop')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 10))
model.fit(x, x, epochs=5, verbose=0)
out = model.predict(x)
out -= K.eval(norm.beta)
out /= K.eval(norm.gamma)
astert_allclose(out.mean(), 0.0, atol=1e-1)
astert_allclose(out.std(), 1.0, atol=1e-1)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_mode_twice():
# This is a regression test for issue #4881 with the old
# batch normalization functions in the Theano backend.
model = Sequential()
model.add(normalization.BatchNormalization(input_shape=(10, 5, 5), axis=1))
model.add(normalization.BatchNormalization(input_shape=(10, 5, 5), axis=1))
model.compile(loss='mse', optimizer='sgd')
x = np.random.normal(loc=5.0, scale=10.0, size=(20, 10, 5, 5))
model.fit(x, x, epochs=1, verbose=0)
model.predict(x)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_mode_twice():
# This is a regression test for issue #4881 with the old
# batch normalization functions in the Theano backend.
model = Sequential()
model.add(normalization.BatchNormalization(input_shape=(10, 5, 5), axis=1))
model.add(normalization.BatchNormalization(input_shape=(10, 5, 5), axis=1))
model.compile(loss='mse', optimizer='sgd')
x = np.random.normal(loc=5.0, scale=10.0, size=(20, 10, 5, 5))
model.fit(x, x, epochs=1, verbose=0)
model.predict(x)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_correctness_1d():
model = Sequential()
norm = normalization.BatchNormalization(input_shape=(10,), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='rmsprop')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 10))
model.fit(x, x, epochs=5, verbose=0)
out = model.predict(x)
out -= K.eval(norm.beta)
out /= K.eval(norm.gamma)
astert_allclose(out.mean(), 0.0, atol=1e-1)
astert_allclose(out.std(), 1.0, atol=1e-1)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
@pytest.mark.skipif((K.backend() == 'theano'),
reason='Bug with theano backend')
def test_batchnorm_convnet_no_center_no_scale():
model = Sequential()
norm = normalization.BatchNormalization(axis=-1, center=False, scale=False,
input_shape=(3, 4, 4), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
astert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
astert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_convnet():
model = Sequential()
norm = normalization.BatchNormalization(axis=1, input_shape=(3, 4, 4), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
out -= np.reshape(K.eval(norm.beta), (1, 3, 1, 1))
out /= np.reshape(K.eval(norm.gamma), (1, 3, 1, 1))
astert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
astert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_convnet():
model = Sequential()
norm = normalization.BatchNormalization(axis=1, input_shape=(3, 4, 4), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
out -= np.reshape(K.eval(norm.beta), (1, 3, 1, 1))
out /= np.reshape(K.eval(norm.gamma), (1, 3, 1, 1))
astert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
astert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)
3
View Complete Implementation : normalization_test.py
Copyright MIT License
Author : hello-sea
Copyright MIT License
Author : hello-sea
@keras_test
def test_batchnorm_convnet():
model = Sequential()
norm = normalization.BatchNormalization(axis=1, input_shape=(3, 4, 4), momentum=0.8)
model.add(norm)
model.compile(loss='mse', optimizer='sgd')
# centered on 5.0, variance 10.0
x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
model.fit(x, x, epochs=4, verbose=0)
out = model.predict(x)
out -= np.reshape(K.eval(norm.beta), (1, 3, 1, 1))
out /= np.reshape(K.eval(norm.gamma), (1, 3, 1, 1))
astert_allclose(np.mean(out, axis=(0, 2, 3)), 0.0, atol=1e-1)
astert_allclose(np.std(out, axis=(0, 2, 3)), 1.0, atol=1e-1)