def test_save_load(self):
custom_objects = {
"true_positive": keras_metrics.true_positive(sparse=self.sparse),
"true_negative": keras_metrics.true_negative(sparse=self.sparse),
"false_positive": keras_metrics.false_positive(sparse=self.sparse),
"false_negative": keras_metrics.false_negative(sparse=self.sparse),
"precision": keras_metrics.precision(sparse=self.sparse),
"recall": keras_metrics.recall(sparse=self.sparse),
"f1_score": keras_metrics.f1_score(sparse=self.sparse),
"sin": keras.backend.sin,
"abs": keras.backend.abs,
}
x, y = self.samples(100)
self.model.fit(x, y, epochs=10)
with tempfile.NamedTemporaryFile() as file:
self.model.save(file.name, overwrite=True)
model = keras.models.load_model(file.name,
custom_objects=custom_objects)
expected = self.model.evaluate(x, y)[1:]
received = model.evaluate(x, y)[1:]
self.assertEqual(expected, received)
def test_metrics(self):
tp = keras_metrics.true_positive()
fp = keras_metrics.false_positive()
fn = keras_metrics.false_negative()
precision = keras_metrics.precision()
recall = keras_metrics.recall()
model = keras.models.Sequential()
model.add(keras.layers.Dense(1, activation="sigmoid", input_dim=2))
model.add(keras.layers.Dense(1, activation="softmax"))
model.compile(optimizer="sgd",
loss="binary_crossentropy",
metrics=[tp, fp, fn, precision, recall])
samples = 1000
x = numpy.random.random((samples, 2))
y = numpy.random.randint(2, size=(samples, 1))
model.fit(x, y, epochs=1, batch_size=10)
metrics = model.evaluate(x, y, batch_size=10)[1:]
tp_val = metrics[0]
fp_val = metrics[1]
fn_val = metrics[2]
precision = metrics[3]
recall = metrics[4]
expected_precision = tp_val / (tp_val + fp_val)
expected_recall = tp_val / (tp_val + fn_val)
self.assertAlmostEqual(expected_precision, precision, delta=0.05)
self.assertAlmostEqual(expected_recall, recall, delta=0.05)
def build_model(max_len, label_count, dropout_ratio=0.1, filter_length=50):
text_input = Input(shape=(max_len,), dtype=tf.string)
elmo_embedding = Lambda(ElmoEmbedding, output_shape=(max_len, 1024))(text_input)
dropout = Dropout(dropout_ratio)(elmo_embedding)
conv1d = Conv1D(filter_length, 3, padding='valid', activation='relu', strides=1)(dropout)
global_1d = GlobalMaxPool1D()(conv1d)
dense = Dense(label_count, activation='sigmoid')(global_1d)
text_model = Model(
inputs=text_input,
outputs=dense
)
parallel_model = multi_gpu_model(text_model, gpus=2)
parallel_model.compile(
optimizer='adam',
loss=abs_KL_div,#'binary_crossentropy',
metrics=[
'categorical_accuracy',
precision_m,
recall_m,
f1_m,
'mae',
abs_KL_div,
true_positive(),
false_positive(),
true_negative(),
false_negative(),
'accuracy',
]
)
text_model.summary()
return text_model
def setUp(self):
tp = keras_metrics.true_positive(sparse=self.sparse)
tn = keras_metrics.true_negative(sparse=self.sparse)
fp = keras_metrics.false_positive(sparse=self.sparse)
fn = keras_metrics.false_negative(sparse=self.sparse)
precision = keras_metrics.precision(sparse=self.sparse)
recall = keras_metrics.recall(sparse=self.sparse)
f1 = keras_metrics.f1_score(sparse=self.sparse)
self.model = keras.models.Sequential()
self.model.add(keras.layers.Activation(keras.backend.sin))
self.model.add(keras.layers.Activation(keras.backend.abs))
if self.sparse:
loss = "sparse_categorical_crossentropy"
else:
loss = "binary_crossentropy"
self.model.compile(optimizer="sgd",
loss=loss,
metrics=[tp, tn, fp, fn, precision, recall, f1])
def create_model(input_length):
print('Creating model...')
model = Sequential()
model.add(Dense(64, input_dim=132, init='uniform'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(32, init='uniform'))
model.add(Activation('tanh'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(1, init='uniform'))
model.add(Activation('sigmoid'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
adam = Adam(lr=0.02)
print('Compiling...')
model.compile(loss='binary_crossentropy',
optimizer=Adam(lr=0.002,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False),
metrics=[
'accuracy',
keras_metrics.precision(),
keras_metrics.recall(),
keras_metrics.false_positive(),
keras_metrics.false_negative()
])
#keras_metrics.false_positive(), keras_metrics.false_negative()
return model
test_images,
test_labels,
verbose=2,
callbacks=[KafkaCallback(session_name)])
print('\nTest accuracy:', test_acc)
if __name__ == "__main__":
METRICS = [
'accuracy',
km.precision(),
km.recall(),
km.true_positive(),
km.true_negative(),
km.false_positive(),
km.false_negative(),
]
LOSS = [
(1, tf.keras.losses.BinaryCrossentropy(from_logits=True),
'Binary crossentropy'),
(2, tf.keras.losses.CategoricalCrossentropy(from_logits=True),
'Categorical crossentropy'),
(3, tf.keras.losses.CategoricalHinge(), 'Categorical hinge'),
(4, tf.keras.losses.CosineSimilarity(), 'Cosine similarity'),
(5, tf.keras.losses.Hinge(), 'Hinge'),
(6, tf.keras.losses.Huber(), 'Huber'),
(7, tf.keras.losses.SquaredHinge(), 'Squared hinge'),
(8, tf.keras.losses.LogCosh(), 'Hyperbolic Cosine'),
(9, tf.keras.losses.MeanAbsoluteError(), 'Mean absolute error'),
(10, tf.keras.losses.MeanAbsolutePercentageError(),
def unet(pretrained_weights=None, input_size=(256, 256, 1)):
inputs = Input(input_size)
conv1 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2,
2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2,
2))(conv6))
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2,
2))(conv7))
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2,
2))(conv8))
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(2,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=1e-4),
loss='binary_crossentropy',
metrics=[
keras_metrics.true_positive(),
keras_metrics.true_negative(),
keras_metrics.false_positive(),
keras_metrics.false_negative(), 'accuracy',
keras_metrics.f1_score(),
keras_metrics.precision(),
keras_metrics.recall()
])
#model.summary()
if (pretrained_weights):
model.load_weights(pretrained_weights)
return model
loss=loss,
metrics=metrics)
model.fit(train_images, train_labels, epochs=epochs, batch_size=batch_size, callbacks=[KafkaCallback(session_name)])
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2, callbacks=[KafkaCallback(session_name)])
print('\nTest accuracy:', test_acc)
if __name__ == "__main__":
METRICS = [
'accuracy',
km.precision(label=0),
km.recall(label=0),
km.true_positive(label=0),
km.true_negative(label=0),
km.false_positive(label=0),
km.false_negative(label=0),
]
LOSS = [
(1, tf.keras.losses.BinaryCrossentropy(from_logits=True), 'Binary crossentropy'),
(2, tf.keras.losses.CategoricalCrossentropy(from_logits=True), 'Categorical crossentropy'),
(3, tf.keras.losses.CategoricalHinge(), 'Categorical hinge'),
(4, tf.keras.losses.CosineSimilarity(), 'Cosine similarity'),
(5, tf.keras.losses.Hinge(), 'Hinge'),
(6, tf.keras.losses.Huber(), 'Huber'),
(7, tf.keras.losses.SquaredHinge(), 'Squared hinge'),
(8, tf.keras.losses.LogCosh(), 'Hyperbolic Cosine'),
(9, tf.keras.losses.MeanAbsoluteError(), 'Mean absolute error'),
(10, tf.keras.losses.MeanAbsolutePercentageError(), 'Mean absolute percentage error'),
(11, tf.keras.losses.MeanSquaredError(), 'Mean squared error'),
(12, tf.keras.losses.MeanSquaredLogarithmicError(), 'Mean squared logarithmic error (MSLE)'),
def test_metrics(self):
tp = keras_metrics.true_positive()
tn = keras_metrics.true_negative()
fp = keras_metrics.false_positive()
fn = keras_metrics.false_negative()
precision = keras_metrics.precision()
recall = keras_metrics.recall()
f1 = keras_metrics.f1_score()
model = keras.models.Sequential()
model.add(keras.layers.Activation(keras.backend.sin))
model.add(keras.layers.Activation(keras.backend.abs))
model.compile(optimizer="sgd",
loss="binary_crossentropy",
metrics=[tp, tn, fp, fn, precision, recall, f1])
samples = 10000
batch_size = 100
lim = numpy.pi / 2
x = numpy.random.uniform(0, lim, (samples, 1))
y = numpy.random.randint(2, size=(samples, 1))
model.fit(x, y, epochs=10, batch_size=batch_size)
metrics = model.evaluate(x, y, batch_size=batch_size)[1:]
metrics = list(map(float, metrics))
tp_val = metrics[0]
tn_val = metrics[1]
fp_val = metrics[2]
fn_val = metrics[3]
precision = metrics[4]
recall = metrics[5]
f1 = metrics[6]
expected_precision = tp_val / (tp_val + fp_val)
expected_recall = tp_val / (tp_val + fn_val)
f1_divident = (expected_precision * expected_recall)
f1_divisor = (expected_precision + expected_recall)
expected_f1 = (2 * f1_divident / f1_divisor)
self.assertGreaterEqual(tp_val, 0.0)
self.assertGreaterEqual(fp_val, 0.0)
self.assertGreaterEqual(fn_val, 0.0)
self.assertGreaterEqual(tn_val, 0.0)
self.assertEqual(sum(metrics[0:4]), samples)
places = 4
self.assertAlmostEqual(expected_precision, precision, places=places)
self.assertAlmostEqual(expected_recall, recall, places=places)
self.assertAlmostEqual(expected_f1, f1, places=places)
model.save('test.hdf5', overwrite=True)
del model
custom_objects = {
"true_positive": keras_metrics.true_positive(),
"true_negative": keras_metrics.true_negative(),
"false_positive": keras_metrics.false_negative(),
"false_negative": keras_metrics.false_negative(),
"precision": keras_metrics.precision(),
"recall": keras_metrics.recall(),
"f1_score": keras_metrics.f1_score(),
"sin": keras.backend.sin,
"abs": keras.backend.abs,
}
model = load_model('test.hdf5', custom_objects=custom_objects)