Closed dmadeka closed 6 years ago
dmadeka
commented
6 years ago @sandeep-krishnamurthy Btw, the pytest.ini had some broken params, so to test I had to comment out a few things (I didnt push those changes):
addopts=-v
#-n 2
#--durations=10
#--cov-report term-missing
#--cov=keras
sandeep-krishnamurthy
commented
6 years ago @dmadeka - You need to install test dependencies to get those options working, they are not broken.
dmadeka
commented
6 years ago Done! Had to modify a good bit because of that:
py.test tests/keras/layers/test_convolutional.py
============================================================================= slowest 10 test durations =============================================================================
39.37s call tests/keras/layers/test_convolutional.py::test_atrous_conv_1d
28.68s call tests/keras/layers/test_convolutional.py::test_upsampling_3d
24.44s call tests/keras/layers/test_convolutional.py::test_deconvolution_2d
22.72s call tests/keras/layers/test_convolutional.py::test_separable_conv_2d
11.94s call tests/keras/layers/test_convolutional.py::test_convolution_3d
10.24s call tests/keras/layers/test_convolutional.py::test_convolution_2d
9.39s call tests/keras/layers/test_convolutional.py::test_convolution_1d
3.65s call tests/keras/layers/test_convolutional.py::test_globalpooling_3d
2.44s call tests/keras/layers/test_convolutional.py::test_maxpooling_1d
2.28s call tests/keras/layers/test_convolutional.py::test_globalpooling_2d
====================================================================== 22 passed, 3 skipped in 114.08 seconds =======================================================================Running the full suite now
dmadeka
commented
6 years ago Full test Failures
___________________________________________________________________________ test_temporal_classification ____________________________________________________________________________
[gw0] darwin -- Python 3.6.1 /Users/maded/anaconda/bin/python
@keras_test
def test_temporal_classification():
'''
Classify temporal sequences of float numbers
of length 3 into 2 classes using
single layer of GRU units and softmax applied
to the last activations of the units
'''
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=500,
nb_test=500,
input_shape=(3, 5),
classification=True,
nb_class=2)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model = Sequential()
model.add(GRU(y_train.shape[-1],
input_shape=(X_train.shape[1], X_train.shape[2]),
activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adagrad',
metrics=['accuracy'])
history = model.fit(X_train, y_train, nb_epoch=20, batch_size=32,
validation_data=(X_test, y_test),
verbose=0)
> assert(history.history['val_acc'][-1] >= 0.8)
E assert 0.7260000009536743 >= 0.8
tests/integration_tests/test_temporal_data_tasks.py:44: AssertionError
_______________________________________________________________________________ TestBackend.test_rnn ________________________________________________________________________________
[gw1] darwin -- Python 3.6.1 /Users/maded/anaconda/bin/python
self = <test_backends.TestBackend object at 0x1216b4da0>
def test_rnn(self):
# implement a simple RNN
input_dim = 8
output_dim = 4
timesteps = 5
input_val = np.random.random((32, timesteps, input_dim))
init_state_val = np.random.random((32, output_dim))
W_i_val = np.random.random((input_dim, output_dim))
W_o_val = np.random.random((output_dim, output_dim))
def rnn_step_fn(input_dim, output_dim, K):
W_i = K.variable(W_i_val)
W_o = K.variable(W_o_val)
def step_function(x, states):
assert len(states) == 1
prev_output = states[0]
output = K.dot(x, W_i) + K.dot(prev_output, W_o)
return output, [output]
return step_function
# test default setup
th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
th_inputs = KTH.variable(input_val)
th_initial_states = [KTH.variable(init_state_val)]
last_output, outputs, new_states = KTH.rnn(th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=None)
th_last_output = KTH.eval(last_output)
th_outputs = KTH.eval(outputs)
assert len(new_states) == 1
th_state = KTH.eval(new_states[0])
tf_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTF)
tf_inputs = KTF.variable(input_val)
tf_initial_states = [KTF.variable(init_state_val)]
last_output, outputs, new_states = KTF.rnn(tf_rnn_step_fn, tf_inputs,
tf_initial_states,
go_backwards=False,
mask=None)
tf_last_output = KTF.eval(last_output)
tf_outputs = KTF.eval(outputs)
assert len(new_states) == 1
tf_state = KTF.eval(new_states[0])
assert_allclose(tf_last_output, th_last_output, atol=1e-04)
assert_allclose(tf_outputs, th_outputs, atol=1e-04)
assert_allclose(tf_state, th_state, atol=1e-04)
# test unroll
unrolled_last_output, unrolled_outputs, unrolled_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=None,
unroll=True,
input_length=timesteps)
unrolled_th_last_output = KTH.eval(unrolled_last_output)
unrolled_th_outputs = KTH.eval(unrolled_outputs)
assert len(unrolled_new_states) == 1
unrolled_th_state = KTH.eval(unrolled_new_states[0])
assert_allclose(th_last_output, unrolled_th_last_output, atol=1e-04)
assert_allclose(th_outputs, unrolled_th_outputs, atol=1e-04)
assert_allclose(th_state, unrolled_th_state, atol=1e-04)
# test go_backwards
th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
th_inputs = KTH.variable(input_val)
th_initial_states = [KTH.variable(init_state_val)]
last_output, outputs, new_states = KTH.rnn(th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=True,
mask=None)
th_last_output = KTH.eval(last_output)
th_outputs = KTH.eval(outputs)
assert len(new_states) == 1
th_state = KTH.eval(new_states[0])
tf_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTF)
tf_inputs = KTF.variable(input_val)
tf_initial_states = [KTF.variable(init_state_val)]
last_output, outputs, new_states = KTF.rnn(tf_rnn_step_fn, tf_inputs,
tf_initial_states,
go_backwards=True,
mask=None)
tf_last_output = KTF.eval(last_output)
tf_outputs = KTF.eval(outputs)
assert len(new_states) == 1
tf_state = KTF.eval(new_states[0])
assert_allclose(tf_last_output, th_last_output, atol=1e-04)
assert_allclose(tf_outputs, th_outputs, atol=1e-04)
assert_allclose(tf_state, th_state, atol=1e-04)
# test unroll with backwards = True
bwd_last_output, bwd_outputs, bwd_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=True,
mask=None)
bwd_th_last_output = KTH.eval(bwd_last_output)
bwd_th_outputs = KTH.eval(bwd_outputs)
assert len(bwd_new_states) == 1
bwd_th_state = KTH.eval(bwd_new_states[0])
bwd_unrolled_last_output, bwd_unrolled_outputs, bwd_unrolled_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=True,
mask=None,
unroll=True,
input_length=timesteps)
bwd_unrolled_th_last_output = KTH.eval(bwd_unrolled_last_output)
bwd_unrolled_th_outputs = KTH.eval(bwd_unrolled_outputs)
assert len(bwd_unrolled_new_states) == 1
bwd_unrolled_th_state = KTH.eval(bwd_unrolled_new_states[0])
assert_allclose(bwd_th_last_output, bwd_unrolled_th_last_output, atol=1e-04)
assert_allclose(bwd_th_outputs, bwd_unrolled_th_outputs, atol=1e-04)
assert_allclose(bwd_th_state, bwd_unrolled_th_state, atol=1e-04)
pick 63894994 Fix bug in Lambda output layer shape
Fix bug in Lambda output layer shape
# test unroll with masking
np_mask = np.random.randint(2, size=(32, timesteps))
th_mask = KTH.variable(np_mask)
masked_last_output, masked_outputs, masked_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=th_mask)
masked_th_last_output = KTH.eval(masked_last_output)
masked_th_outputs = KTH.eval(masked_outputs)
assert len(masked_new_states) == 1
masked_th_state = KTH.eval(masked_new_states[0])
unrolled_masked_last_output, unrolled_masked_outputs, unrolled_masked_new_states = KTH.rnn(
th_rnn_step_fn, th_inputs,
th_initial_states,
go_backwards=False,
mask=th_mask,
unroll=True,
input_length=timesteps)
unrolled_masked_th_last_output = KTH.eval(unrolled_masked_last_output)
unrolled_masked_th_outputs = KTH.eval(unrolled_masked_outputs)
assert len(unrolled_masked_new_states) == 1
unrolled_masked_th_state = KTH.eval(unrolled_masked_new_states[0])
> assert_allclose(unrolled_masked_th_last_output, masked_th_last_output, atol=1e-04)
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=0.0001
E
E (mismatch 6.25%)
E x: array([[16.388945, 19.627626, 19.422802, 20.497753],
E [27.255352, 32.8809 , 33.373707, 36.79008 ],
E [ 6.731161, 7.887591, 8.563793, 8.687819],...
E y: array([[1.638894e+01, 1.962763e+01, 1.942280e+01, 2.049775e+01],
E [2.725535e+01, 3.288090e+01, 3.337371e+01, 3.679008e+01],
E [6.731161e+00, 7.887591e+00, 8.563793e+00, 8.687819e+00],...
tests/keras/backend/test_backends.py:459: AssertionError
====================================================== 2 failed, 218 passed, 35 skipped, 6 xpassed, 2 error in 896.64 seconds =======================================================
@sandeep-krishnamurthy Fixes the bugs the last commit caused in output shape inference for a Lambda layer