def on_epoch_end(self, epoch, logs=None):
self.losses.append(logs.get('loss'))
self.num_epochs = epoch
self.losses.append(logs.get('loss'))
if epoch % 6 != 0: return
# return
intermediate_layer_model = Model(
inputs=self.model.input,
outputs=self.model.get_layer('abundances').output)
abundances = intermediate_layer_model.predict(self.input)
endmembers = self.model.layers[len(self.model.layers) -
1].get_weights()[0]
plotHist(self.losses, 33)
if self.plotGT:
dict = order_endmembers(endmembers, self.endmembersGT)
# if self.is_GT_for_A:
# plotAbundances(self.num_endmembers, self.size_data, abundances, self.abundancesGT, dict, self.use_ASC)
# else:
# plotAbundances(self.num_endmembers, self.size_data, abundances, None, None, self.use_ASC, is_GT=False)
plotEndmembersAndGT(endmembers, self.endmembersGT, dict)
# plotAbundancesSimple(self.num_endmembers, self.size_data, abundances, dict, use_ASC=1, figure_nr=10)
else:
# plotAbundances(self.num_endmembers, self.size_data, abundances, None, None, self.use_ASC)
plotEndmembers(self.num_endmembers, endmembers)
print(K.eval(self.model._collected_trainable_weights[-3]))
return
def test_tf_map_coordinates():
np.random.seed(42)
input = np.random.random((100, 100))
coords = np.random.random((10000, 2)) * 99
sp_mapped_vals = map_coordinates(input, coords.T, order=1)
tf_mapped_vals = tf_map_coordinates(K.variable(input), K.variable(coords))
assert np.allclose(sp_mapped_vals, K.eval(tf_mapped_vals), atol=1e-5)
def test_tf_batch_map_offsets():
np.random.seed(42)
input = np.random.random((4, 100, 100))
offsets = np.random.random((4, 100, 100, 2)) * 2
sp_mapped_vals = sp_batch_map_offsets(input, offsets)
tf_mapped_vals = tf_batch_map_offsets(K.variable(input),
K.variable(offsets))
assert np.allclose(sp_mapped_vals, K.eval(tf_mapped_vals), atol=1e-5)
def test_tf_batch_map_offsets_grad():
np.random.seed(42)
input = np.random.random((4, 100, 100))
offsets = np.random.random((4, 100, 100, 2)) * 2
input = K.variable(input)
offsets = K.variable(offsets)
tf_mapped_vals = tf_batch_map_offsets(input, offsets)
grad = K.gradients(tf_mapped_vals, input)[0]
grad = K.eval(grad)
assert not np.allclose(grad, 0)