def test_raw_moore_penrose(self):
from dlnn.tests.ml.repos_helper import corr_step_11_a, categorical_label_init
from dlnn.util import MoorePenrose
x = corr_step_11_a
t = categorical_label_init
r = K.dot(MoorePenrose.pinv2(K.variable(x), 1e-31), K.variable(t))
self.assertIsNotNone(r)
def test_training_model_bka_to_step_12_c(self):
from keras import Model
from dlnn.tests.ml.cnn_func_test import inputs
from dlnn.util import MoorePenrose
#
# Feed Beta
#
feed = Model(inputs=inputs, outputs=step_11_c_activation)
output = feed.predict(normalized)
w_10_b = feed.get_layer(index=10).get_weights()
w_12_b = [K.eval(K.dot(MoorePenrose.pinv3(output), K.variable(categorical_label_init)))]
for i in range(12):
layer = feed.get_layer(index=i).get_weights()
self.assertIsNotNone(layer)
# print("step_%d" % (i + 1), layer)
#
# Training Model
#
network = Model(inputs=inputs, outputs=step_12_c_dense)
network.compile(optimizer=keras.optimizers.RMSprop(lr=0.0, rho=0.0, epsilon=None, decay=0.0),
loss=keras.losses.categorical_crossentropy,
metrics=[keras.metrics.categorical_accuracy, keras.metrics.mape])
network.get_layer(index=10).set_weights(w_10_b)
network.get_layer(index=12).set_weights(w_12_b)
network.fit(normalized, categorical_label_init, batch_size=normalized.shape[0])
self.assertTrue(numpy.allclose(w_10_b[0], network.get_layer(index=10).get_weights()[0], rtol=0))
self.assertTrue(numpy.allclose(w_12_b[0], network.get_layer(index=12).get_weights()[0], rtol=0))
result = network.predict(normalized, batch_size=normalized.shape[0])
self.assertIsNotNone(result)
def test_input_to_beta_c(self):
from keras import Model
from dlnn.util import MoorePenrose
network = Model(inputs=inputs, outputs=step_11_c_activation)
network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
output = network.predict(normalized)
beta = K.dot(MoorePenrose.pinv3(output), K.variable(categorical_label_init))
self.assertIsNotNone(beta)
def __train(self, x, y):
assert self.layer is not None
yc = to_categorical(y, self.category_num)
elm_1_beta_net = Model(inputs=self.layer['input'],
outputs=self.layer['elm_1_activation_1'])
elm_1_activation_1_o = elm_1_beta_net.predict(x)
elm_1_dense_1_w = elm_1_beta_net.get_layer(
name='elm_1_dense_1').get_weights()
elm_1_dense_2_w = [
K.eval(K.dot(MoorePenrose.pinv3(elm_1_activation_1_o), yc))
]
elm_2_beta_net = Model(inputs=self.layer['input'],
outputs=self.layer['elm_2_activation_1'])
elm_2_activation_1_o = elm_2_beta_net.predict(x)
elm_2_dense_1_w = elm_2_beta_net.get_layer(
name='elm_2_dense_1').get_weights()
elm_2_dense_2_w = [
K.eval(K.dot(MoorePenrose.pinv3(elm_2_activation_1_o), yc))
]
elm_3_beta_net = Model(inputs=self.layer['input'],
outputs=self.layer['elm_3_activation_1'])
elm_3_activation_1_o = elm_3_beta_net.predict(x)
elm_3_dense_1_w = elm_3_beta_net.get_layer(
name='elm_3_dense_1').get_weights()
elm_3_dense_2_w = [
K.eval(K.dot(MoorePenrose.pinv3(elm_3_activation_1_o), yc))
]
network = Model(inputs=self.layer['input'],
outputs=self.layer['fully_connected_merge'])
network.compile(optimizer=RMSprop(lr=0.0,
rho=0.0,
epsilon=None,
decay=0.0),
loss=categorical_crossentropy,
metrics=[categorical_accuracy, mape])
network.get_layer(name='elm_1_dense_1').set_weights(elm_1_dense_1_w)
network.get_layer(name='elm_1_dense_2').set_weights(elm_1_dense_2_w)
network.get_layer(name='elm_2_dense_1').set_weights(elm_2_dense_1_w)
network.get_layer(name='elm_2_dense_2').set_weights(elm_2_dense_2_w)
network.get_layer(name='elm_3_dense_1').set_weights(elm_3_dense_1_w)
network.get_layer(name='elm_3_dense_2').set_weights(elm_3_dense_2_w)
network.fit(x, yc)
network.save(self.network_path)
def test_manual_merge_categorical_value(self):
from keras import Model
from dlnn.tests.ml.cnn_func_test import inputs
from dlnn.util import MoorePenrose
import tensorflow as tf
#
# Feed Beta
#
feed = Model(inputs=inputs, outputs=step_11_a_activation)
output = feed.predict(normalized)
w_10_a = feed.get_layer(index=10).get_weights()
w_12_a = [K.eval(K.dot(MoorePenrose.pinv3(output), K.variable(categorical_label_init)))]
feed = Model(inputs=inputs, outputs=step_11_b_activation)
output = feed.predict(normalized)
w_10_b = feed.get_layer(index=10).get_weights()
w_12_b = [K.eval(K.dot(MoorePenrose.pinv3(output), K.variable(categorical_label_init)))]
feed = Model(inputs=inputs, outputs=step_11_c_activation)
output = feed.predict(normalized)
w_10_c = feed.get_layer(index=10).get_weights()
w_12_c = [K.eval(K.dot(MoorePenrose.pinv3(output), K.variable(categorical_label_init)))]
#
# Training Model
#
network = Model(inputs=inputs, outputs=step_14_reshape)
network.compile(optimizer=keras.optimizers.RMSprop(lr=0.0, rho=0.0, epsilon=None, decay=0.0),
loss=keras.losses.categorical_crossentropy,
metrics=[keras.metrics.categorical_accuracy, keras.metrics.mape])
network.get_layer(index=10).set_weights(w_10_a)
network.get_layer(index=11).set_weights(w_10_b)
network.get_layer(index=12).set_weights(w_10_c)
network.get_layer(index=16).set_weights(w_12_a)
network.get_layer(index=17).set_weights(w_12_b)
network.get_layer(index=18).set_weights(w_12_c)
result = network.predict(normalized, batch_size=normalized.shape[0])
# print(result)
result = K.cast(K.argmax(result), dtype=tf.int32)
# print(K.eval(result))
result = tf.map_fn(lambda x: tf.bincount(x, minlength=3), result)
# print(K.eval(result))
result = K.argmax(result)
# print(K.eval(result))
self.assertIsNotNone(result)
def test_baked_dlnndump(self):
from keras import Model
from dlnn.util import MoorePenrose
#
# Feed Beta
#
feed = Model(inputs=inputs, outputs=step_11_a_activation)
output = feed.predict(corpus_data)
w_10_a = feed.get_layer(index=13).get_weights()
w_12_a = [
K.eval(
K.dot(MoorePenrose.pinv3(output),
to_categorical(label_init, 3)))
]
feed = Model(inputs=inputs, outputs=step_11_b_activation)
output = feed.predict(corpus_data)
w_10_b = feed.get_layer(index=13).get_weights()
w_12_b = [
K.eval(
K.dot(MoorePenrose.pinv3(output),
to_categorical(label_init, 3)))
]
feed = Model(inputs=inputs, outputs=step_11_c_activation)
output = feed.predict(corpus_data)
w_10_c = feed.get_layer(index=13).get_weights()
w_12_c = [
K.eval(
K.dot(MoorePenrose.pinv3(output),
to_categorical(label_init, 3)))
]
self.assertTrue(True)
from dlnn.Dlnn import DLNN_DEFAULT_CONFIG
yc = keras.utils.to_categorical(label_init,
len(numpy.unique(label_init)))
dlnn = DlnnDump(**DLNN_DEFAULT_CONFIG)
# dlnn.train(corpus_data, corpus_label - 1)
network = dlnn.get_model()
train_eval = network.evaluate(corpus_data, yc)
self.assertIsNotNone(train_eval)
# network.summary()
# print(train_eval)
from keras import Model
layer_name = 'pre_tiling'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output, normalized, rtol=1e-6))
layer_name = 'cnn_conv_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output[0], corr_step_1, rtol=1e-6))
layer_name = 'cnn_activation_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output[0], corr_step_2, rtol=1e-6))
layer_name = 'cnn_conv_2'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output[0], corr_step_3, rtol=1e-6))
layer_name = 'cnn_activation_2'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output[0], corr_step_4, rtol=1e-6))
layer_name = 'cnn_pooling_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output[0], corr_step_5, rtol=1e-6))
layer_name = 'cnn_conv_3'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output[0], corr_step_6, rtol=1e-6))
layer_name = 'cnn_activation_3'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output[0], corr_step_7, rtol=1e-6))
layer_name = 'cnn_pooling_2'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output[0], corr_step_8, rtol=1e-6))
self.assertTrue(
numpy.allclose(intermediate_output, corr_step_8_full, rtol=1e-6))
layer_name = 'bridge_flatten'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output, corr_step_9, rtol=1e-6))
layer_name = 'elm_1_dense_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output, corr_step_10_a, rtol=1e-6))
self.assertTrue(
numpy.allclose(corr_step_10_a_kernel_init,
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
self.assertTrue(
numpy.allclose(corr_step_10_a_bias_init,
network.get_layer(layer_name).get_weights()[1],
rtol=1e-6))
self.assertTrue(
numpy.allclose(w_10_a[0],
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
self.assertTrue(
numpy.allclose(w_10_a[1],
network.get_layer(layer_name).get_weights()[1],
rtol=1e-6))
layer_name = 'elm_1_activation_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output, corr_step_11_a, rtol=1e-6))
layer_name = 'elm_2_dense_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output, corr_step_10_b, rtol=1e-6))
self.assertTrue(
numpy.allclose(corr_step_10_b_kernel_init,
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
self.assertTrue(
numpy.allclose(corr_step_10_b_bias_init,
network.get_layer(layer_name).get_weights()[1],
rtol=1e-6))
self.assertTrue(
numpy.allclose(w_10_b[0],
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
self.assertTrue(
numpy.allclose(w_10_b[1],
network.get_layer(layer_name).get_weights()[1],
rtol=1e-6))
layer_name = 'elm_2_activation_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output, corr_step_11_b, rtol=1e-6))
layer_name = 'elm_3_dense_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output, corr_step_10_c, rtol=1e-6))
self.assertTrue(
numpy.allclose(corr_step_10_c_kernel_init,
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
self.assertTrue(
numpy.allclose(corr_step_10_c_bias_init,
network.get_layer(layer_name).get_weights()[1],
rtol=1e-6))
self.assertTrue(
numpy.allclose(w_10_c[0],
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
self.assertTrue(
numpy.allclose(w_10_c[1],
network.get_layer(layer_name).get_weights()[1],
rtol=1e-6))
layer_name = 'elm_3_activation_1'
intermediate = Model(inputs=network.input,
outputs=network.get_layer(layer_name).output)
intermediate_output = intermediate.predict(corpus_data)
self.assertTrue(
numpy.allclose(intermediate_output, corr_step_11_c, rtol=1e-6))
layer_name = 'elm_1_dense_2'
self.assertTrue(
numpy.allclose(w_12_a[0],
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
layer_name = 'elm_2_dense_2'
self.assertTrue(
numpy.allclose(w_12_b[0],
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
layer_name = 'elm_3_dense_2'
self.assertTrue(
numpy.allclose(w_12_c[0],
network.get_layer(layer_name).get_weights()[0],
rtol=1e-6))
def test_functional_dlnn(self):
from keras import Model
from dlnn.util import MoorePenrose
#
# Feed Beta
#
feed = Model(inputs=inputs, outputs=step_11_a_activation)
output = feed.predict(corpus_data)
w_10_a = feed.get_layer(index=13).get_weights()
w_12_a = [
K.eval(
K.dot(MoorePenrose.pinv3(output),
to_categorical(label_init, 3)))
]
feed = Model(inputs=inputs, outputs=step_11_b_activation)
output = feed.predict(corpus_data)
w_10_b = feed.get_layer(index=13).get_weights()
w_12_b = [
K.eval(
K.dot(MoorePenrose.pinv3(output),
to_categorical(label_init, 3)))
]
feed = Model(inputs=inputs, outputs=step_11_c_activation)
output = feed.predict(corpus_data)
w_10_c = feed.get_layer(index=13).get_weights()
w_12_c = [
K.eval(
K.dot(MoorePenrose.pinv3(output),
to_categorical(label_init, 3)))
]
#
# Training Model
#
network = Model(inputs=inputs, outputs=step_15_output)
network.compile(
optimizer=keras.optimizers.RMSprop(lr=0.0,
rho=0.0,
epsilon=None,
decay=0.0),
loss=keras.losses.categorical_crossentropy,
metrics=[keras.metrics.categorical_accuracy, keras.metrics.mape])
for i in range(25):
layer = network.get_layer(index=i).get_weights()
self.assertIsNotNone(layer)
# print("step_%d" % (i + 1), layer)
network.get_layer(index=13).set_weights(w_10_a)
network.get_layer(index=14).set_weights(w_10_b)
network.get_layer(index=15).set_weights(w_10_c)
network.get_layer(index=19).set_weights(w_12_a)
network.get_layer(index=20).set_weights(w_12_b)
network.get_layer(index=21).set_weights(w_12_c)
network.fit(corpus_data,
to_categorical(label_init, 3),
batch_size=corpus_data.shape[0])
self.assertTrue(
numpy.allclose(w_10_a[0],
network.get_layer(index=13).get_weights()[0],
rtol=0))
self.assertTrue(
numpy.allclose(w_10_b[0],
network.get_layer(index=14).get_weights()[0],
rtol=0))
self.assertTrue(
numpy.allclose(w_10_c[0],
network.get_layer(index=15).get_weights()[0],
rtol=0))
self.assertTrue(
numpy.allclose(w_12_a[0],
network.get_layer(index=19).get_weights()[0],
rtol=0))
self.assertTrue(
numpy.allclose(w_12_b[0],
network.get_layer(index=20).get_weights()[0],
rtol=0))
self.assertTrue(
numpy.allclose(w_12_c[0],
network.get_layer(index=21).get_weights()[0],
rtol=0))
result = network.predict(corpus_data, batch_size=corpus_data.shape[0])
# print(result.argmax(axis=1))
self.assertIsNotNone(result)