def Build_Model_DNN_Image(shape, number_of_classes, sparse_categorical,
min_hidden_layer_dnn, max_hidden_layer_dnn,
min_nodes_dnn, max_nodes_dnn, random_optimizor,
dropout):
'''
buildModel_DNN_image(shape, number_of_classes,sparse_categorical)
Build Deep neural networks Model for text classification
Shape is input feature space
number_of_classes is number of classes
'''
model = Sequential()
values = list(range(min_nodes_dnn, max_nodes_dnn))
Numberof_NOde = random.choice(values)
Lvalues = list(range(min_hidden_layer_dnn, max_hidden_layer_dnn))
nLayers = random.choice(Lvalues)
print(shape)
model.add(Flatten(input_shape=shape))
model.add(Dense(Numberof_NOde, activation='relu'))
model.add(Dropout(dropout))
for i in range(0, nLayers - 1):
Numberof_NOde = random.choice(values)
model.add(Dense(Numberof_NOde, activation='relu'))
model.add(Dropout(dropout))
if number_of_classes == 2:
model.add(Dense(1, activation='sigmoid'))
model_tmp = model
model.compile(loss='binary_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.binary_precision(),
km.binary_recall(),
km.binary_f1_score(),
km.binary_true_positive(),
km.binary_true_negative(),
km.binary_false_positive(),
km.binary_false_negative()
])
else:
model.add(Dense(number_of_classes, activation='softmax'))
model_tmp = model
if sparse_categorical:
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.sparse_categorical_precision(),
km.sparse_categorical_recall(),
km.sparse_categorical_f1_score(),
km.sparse_categorical_true_positive(),
km.sparse_categorical_true_negative(),
km.sparse_categorical_false_positive(),
km.sparse_categorical_false_negative()
])
else:
model.compile(loss='categorical_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.categorical_precision(),
km.categorical_recall(),
km.categorical_f1_score(),
km.categorical_true_positive(),
km.categorical_true_negative(),
km.categorical_false_positive(),
km.categorical_false_negative()
])
return model, model_tmp
s.run(tf.compat.v1.global_variables_initializer())
model.fit(train_images, train_labels, epochs=epochs, batch_size=batch_size,
callbacks=[KafkaCallback(session_name)])
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.categorical_precision(),
km.categorical_recall(),
km.categorical_true_positive(),
km.categorical_true_negative(),
km.categorical_false_positive(),
km.categorical_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(), 'Mean absolute percentage error'),
def Build_Model_RNN_Text(word_index,
embedding_index,
number_of_classes,
MAX_SEQUENCE_LENGTH,
EMBEDDING_DIM,
sparse_categorical,
min_hidden_layer_rnn,
max_hidden_layer_rnn,
min_nodes_rnn,
max_nodes_rnn,
random_optimizor,
dropout,
use_cuda=True,
use_bidirectional=True,
_l2=0.01,
lr=1e-3):
"""
def buildModel_RNN(word_index, embedding_index, number_of_classes, MAX_SEQUENCE_LENGTH, EMBEDDING_DIM, sparse_categorical):
word_index in word index ,
embedding_index is embeddings index, look at data_helper.py
number_of_classes is number of classes,
MAX_SEQUENCE_LENGTH is maximum lenght of text sequences
"""
Recurrent = CuDNNGRU if use_cuda else GRU
model = Sequential()
values = list(range(min_nodes_rnn, max_nodes_rnn + 1))
values_layer = list(range(min_hidden_layer_rnn - 1, max_hidden_layer_rnn))
layer = random.choice(values_layer)
print(layer)
embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embedding_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
else:
embedding_matrix[i] = embedding_index['UNK']
model.add(
Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=True))
gru_node = random.choice(values)
print(gru_node)
for i in range(0, layer):
if use_bidirectional:
model.add(
Bidirectional(
Recurrent(gru_node,
return_sequences=True,
kernel_regularizer=l2(_l2))))
else:
model.add(
Recurrent(gru_node,
return_sequences=True,
kernel_regularizer=l2(_l2)))
model.add(Dropout(dropout))
if use_bidirectional:
model.add(
Bidirectional(Recurrent(gru_node, kernel_regularizer=l2(_l2))))
else:
model.add(Recurrent(gru_node, kernel_regularizer=l2(_l2)))
model.add(Dropout(dropout))
model.add(Dense(256, activation='relu', kernel_regularizer=l2(_l2)))
if number_of_classes == 2:
model.add(Dense(1, activation='sigmoid', kernel_regularizer=l2(_l2)))
model_tmp = model
model.compile(loss='binary_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.binary_precision(),
km.binary_recall(),
km.binary_f1_score(),
km.binary_true_positive(),
km.binary_true_negative(),
km.binary_false_positive(),
km.binary_false_negative()
])
else:
model.add(
Dense(number_of_classes,
activation='softmax',
kernel_regularizer=l2(_l2)))
model_tmp = model
if sparse_categorical:
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.sparse_categorical_precision(),
km.sparse_categorical_recall(),
km.sparse_categorical_f1_score(),
km.sparse_categorical_true_positive(),
km.sparse_categorical_true_negative(),
km.sparse_categorical_false_positive(),
km.sparse_categorical_false_negative()
])
else:
model.compile(loss='categorical_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.categorical_precision(),
km.categorical_recall(),
km.categorical_f1_score(),
km.categorical_true_positive(),
km.categorical_true_negative(),
km.categorical_false_positive(),
km.categorical_false_negative()
])
return model, model_tmp
def Build_Model_CNN_Text(word_index,
embedding_index,
number_of_classes,
MAX_SEQUENCE_LENGTH,
EMBEDDING_DIM,
sparse_categorical,
min_hidden_layer_cnn,
max_hidden_layer_cnn,
min_nodes_cnn,
max_nodes_cnn,
random_optimizor,
dropout,
simple_model=False,
_l2=0.01,
lr=1e-3):
"""
def buildModel_CNN(word_index,embedding_index,number_of_classes,MAX_SEQUENCE_LENGTH,EMBEDDING_DIM,Complexity=0):
word_index in word index ,
embedding_index is embeddings index, look at data_helper.py
number_of_classes is number of classes,
MAX_SEQUENCE_LENGTH is maximum lenght of text sequences,
EMBEDDING_DIM is an int value for dimention of word embedding look at data_helper.py
Complexity we have two different CNN model as follows
F=0 is simple CNN with [1 5] hidden layer
Complexity=2 is more complex model of CNN with filter_length of range [1 10]
"""
model = Sequential()
if simple_model:
embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embedding_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
else:
embedding_matrix[i] = embedding_index['UNK']
model.add(
Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=True))
values = list(range(min_nodes_cnn, max_nodes_cnn))
Layer = list(range(min_hidden_layer_cnn, max_hidden_layer_cnn))
Layer = random.choice(Layer)
for i in range(0, Layer):
Filter = random.choice(values)
model.add(
Conv1D(Filter,
5,
activation='relu',
kernel_regularizer=l2(_l2)))
model.add(Dropout(dropout))
model.add(MaxPooling1D(5))
model.add(Flatten())
Filter = random.choice(values)
model.add(Dense(Filter, activation='relu', kernel_regularizer=l2(_l2)))
model.add(Dropout(dropout))
Filter = random.choice(values)
model.add(Dense(Filter, activation='relu', kernel_regularizer=l2(_l2)))
model.add(Dropout(dropout))
if number_of_classes == 2:
model.add(
Dense(1, activation='sigmoid', kernel_regularizer=l2(_l2)))
model_tmp = model
model.compile(loss='binary_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.binary_precision(),
km.binary_recall(),
km.binary_f1_score(),
km.binary_true_positive(),
km.binary_true_negative(),
km.binary_false_positive(),
km.binary_false_negative()
])
else:
model.add(
Dense(number_of_classes,
activation='softmax',
kernel_regularizer=l2(_l2)))
model_tmp = model
if sparse_categorical:
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.sparse_categorical_precision(),
km.sparse_categorical_recall(),
km.sparse_categorical_f1_score(),
km.sparse_categorical_true_positive(),
km.sparse_categorical_true_negative(),
km.sparse_categorical_false_positive(),
km.sparse_categorical_false_negative()
])
else:
model.compile(loss='categorical_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.categorical_precision(),
km.categorical_recall(),
km.categorical_f1_score(),
km.categorical_true_positive(),
km.categorical_true_negative(),
km.categorical_false_positive(),
km.categorical_false_negative()
])
else:
embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embedding_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
else:
embedding_matrix[i] = embedding_index['UNK']
embedding_layer = Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=True)
# applying a more complex convolutional approach
convs = []
values_layer = list(range(min_hidden_layer_cnn, max_hidden_layer_cnn))
filter_sizes = []
layer = random.choice(values_layer)
print("Filter ", layer)
for fl in range(0, layer):
filter_sizes.append((fl + 2))
values_node = list(range(min_nodes_cnn, max_nodes_cnn))
node = random.choice(values_node)
print("Node ", node)
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH, ), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
for fsz in filter_sizes:
l_conv = Conv1D(node, kernel_size=fsz,
activation='relu')(embedded_sequences)
l_pool = MaxPooling1D(5)(l_conv)
#l_pool = Dropout(0.25)(l_pool)
convs.append(l_pool)
l_merge = Concatenate(axis=1)(convs)
l_cov1 = Conv1D(node, 5, activation='relu')(l_merge)
l_cov1 = Dropout(dropout)(l_cov1)
l_pool1 = MaxPooling1D(5)(l_cov1)
l_cov2 = Conv1D(node, 5, activation='relu')(l_pool1)
l_cov2 = Dropout(dropout)(l_cov2)
l_pool2 = MaxPooling1D(30)(l_cov2)
l_flat = Flatten()(l_pool2)
l_dense = Dense(1024, activation='relu')(l_flat)
l_dense = Dropout(dropout)(l_dense)
l_dense = Dense(512, activation='relu')(l_dense)
l_dense = Dropout(dropout)(l_dense)
if number_of_classes == 2:
preds = Dense(1, activation='sigmoid')(l_dense)
else:
preds = Dense(number_of_classes, activation='softmax')(l_dense)
model = Model(sequence_input, preds)
model_tmp = model
if number_of_classes == 2:
model.compile(loss='binary_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.binary_precision(),
km.binary_recall(),
km.binary_f1_score(),
km.binary_true_positive(),
km.binary_true_negative(),
km.binary_false_positive(),
km.binary_false_negative()
])
else:
if sparse_categorical:
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.sparse_categorical_precision(),
km.sparse_categorical_recall(),
km.sparse_categorical_f1_score(),
km.sparse_categorical_true_positive(),
km.sparse_categorical_true_negative(),
km.sparse_categorical_false_positive(),
km.sparse_categorical_false_negative()
])
else:
model.compile(loss='categorical_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.categorical_precision(),
km.categorical_recall(),
km.categorical_f1_score(),
km.categorical_true_positive(),
km.categorical_true_negative(),
km.categorical_false_positive(),
km.categorical_false_negative()
])
return model, model_tmp
def Build_Model_RNN_Image(shape, number_of_classes, sparse_categorical,
min_nodes_rnn, max_nodes_rnn, random_optimizor,
dropout):
"""
def Image_model_RNN(num_classes,shape):
num_classes is number of classes,
shape is (w,h,p)
"""
values = list(range(min_nodes_rnn - 1, max_nodes_rnn))
node = random.choice(values)
x = Input(shape=shape)
# Encodes a row of pixels using TimeDistributed Wrapper.
encoded_rows = TimeDistributed(CuDNNLSTM(node,
recurrent_dropout=dropout))(x)
node = random.choice(values)
# Encodes columns of encoded rows.
encoded_columns = CuDNNLSTM(node, recurrent_dropout=dropout)(encoded_rows)
# Final predictions and model.
#prediction = Dense(256, activation='relu')(encoded_columns)
if number_of_classes == 2:
prediction = Dense(1, activation='sigmoid')(encoded_columns)
else:
prediction = Dense(number_of_classes,
activation='softmax')(encoded_columns)
model = Model(x, prediction)
model_tmp = model
if number_of_classes == 2:
model.compile(loss='binary_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.binary_precision(),
km.binary_recall(),
km.binary_f1_score(),
km.binary_true_positive(),
km.binary_true_negative(),
km.binary_false_positive(),
km.binary_false_negative()
])
else:
if sparse_categorical:
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.sparse_categorical_precision(),
km.sparse_categorical_recall(),
km.sparse_categorical_f1_score(),
km.sparse_categorical_true_positive(),
km.sparse_categorical_true_negative(),
km.sparse_categorical_false_positive(),
km.sparse_categorical_false_negative()
])
else:
model.compile(loss='categorical_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.categorical_precision(),
km.categorical_recall(),
km.categorical_f1_score(),
km.categorical_true_positive(),
km.categorical_true_negative(),
km.categorical_false_positive(),
km.categorical_false_negative()
])
return model, model_tmp
def Build_Model_CNN_Image(shape, number_of_classes, sparse_categorical,
min_hidden_layer_cnn, max_hidden_layer_cnn,
min_nodes_cnn, max_nodes_cnn, random_optimizor,
dropout):
"""""
def Image_model_CNN(num_classes,shape):
num_classes is number of classes,
shape is (w,h,p)
""" ""
model = Sequential()
values = list(range(min_nodes_cnn, max_nodes_cnn))
Layers = list(range(min_hidden_layer_cnn, max_hidden_layer_cnn))
Layer = random.choice(Layers)
Filter = random.choice(values)
model.add(Conv2D(Filter, (3, 3), padding='same', input_shape=shape))
model.add(Activation('relu'))
model.add(Conv2D(Filter, (3, 3)))
model.add(Activation('relu'))
for i in range(0, Layer):
Filter = random.choice(values)
model.add(Conv2D(Filter, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(dropout))
if number_of_classes == 2:
model.add(Dense(1, activation='sigmoid', kernel_constraint=maxnorm(3)))
model_tmp = model
model.compile(loss='binary_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.binary_precision(),
km.binary_recall(),
km.binary_f1_score(),
km.binary_true_positive(),
km.binary_true_negative(),
km.binary_false_positive(),
km.binary_false_negative()
])
else:
model.add(
Dense(number_of_classes,
activation='softmax',
kernel_constraint=maxnorm(3)))
model_tmp = model
if sparse_categorical:
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.sparse_categorical_precision(),
km.sparse_categorical_recall(),
km.sparse_categorical_f1_score(),
km.sparse_categorical_true_positive(),
km.sparse_categorical_true_negative(),
km.sparse_categorical_false_positive(),
km.sparse_categorical_false_negative()
])
else:
model.compile(loss='categorical_crossentropy',
optimizer=optimizors(random_optimizor),
metrics=[
'accuracy',
km.categorical_precision(),
km.categorical_recall(),
km.categorical_f1_score(),
km.categorical_true_positive(),
km.categorical_true_negative(),
km.categorical_false_positive(),
km.categorical_false_negative()
])
return model, model_tmp
def Build_Model_DNN_Text(shape,
number_of_classes,
sparse_categorical,
min_hidden_layer_dnn,
max_hidden_layer_dnn,
min_nodes_dnn,
max_nodes_dnn,
random_optimizor,
dropout,
_l2=0.01,
lr=1e-3):
"""
buildModel_DNN_Tex(shape, number_of_classes,sparse_categorical)
Build Deep neural networks Model for text classification
Shape is input feature space
number_of_classes is number of classes
"""
model = Sequential()
layer = list(range(min_hidden_layer_dnn, max_hidden_layer_dnn))
node = list(range(min_nodes_dnn, max_nodes_dnn))
Numberof_NOde = random.choice(node)
nLayers = random.choice(layer)
Numberof_NOde_old = Numberof_NOde
model.add(
Dense(Numberof_NOde,
input_dim=shape,
activation='relu',
kernel_regularizer=l2(_l2)))
model.add(Dropout(dropout))
for i in range(0, nLayers):
Numberof_NOde = random.choice(node)
model.add(
Dense(Numberof_NOde,
input_dim=Numberof_NOde_old,
activation='relu',
kernel_regularizer=l2(_l2)))
model.add(Dropout(dropout))
Numberof_NOde_old = Numberof_NOde
if number_of_classes == 2:
model.add(Dense(1, activation='sigmoid', kernel_regularizer=l2(_l2)))
model_tmp = model
model.compile(loss='binary_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.binary_precision(),
km.binary_recall(),
km.binary_f1_score(),
km.binary_true_positive(),
km.binary_true_negative(),
km.binary_false_positive(),
km.binary_false_negative()
])
else:
model.add(
Dense(number_of_classes,
activation='softmax',
kernel_regularizer=l2(_l2)))
model_tmp = model
if sparse_categorical:
model.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.sparse_categorical_precision(),
km.sparse_categorical_recall(),
km.sparse_categorical_f1_score(),
km.sparse_categorical_true_positive(),
km.sparse_categorical_true_negative(),
km.sparse_categorical_false_positive(),
km.sparse_categorical_false_negative()
])
else:
model.compile(loss='categorical_crossentropy',
optimizer=optimizors(random_optimizor, lr),
metrics=[
'accuracy',
km.categorical_precision(),
km.categorical_recall(),
km.categorical_f1_score(),
km.categorical_true_positive(),
km.categorical_true_negative(),
km.categorical_false_positive(),
km.categorical_false_negative()
])
return model, model_tmp
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.categorical_precision(),
km.categorical_recall(),
km.categorical_true_positive(label=0),
km.categorical_true_negative(label=0),
km.categorical_false_positive(label=0),
km.categorical_false_negative(label=0),
]
LOSS = [
(1, keras.losses.BinaryCrossentropy(from_logits=True),
'Binary crossentropy'),
(2, keras.losses.CategoricalCrossentropy(from_logits=True),
'Categorical crossentropy'),
(3, keras.losses.CategoricalHinge(), 'Categorical hinge'),
(5, keras.losses.Hinge(), 'Hinge'),
(5, keras.losses.Hinge(), 'Hinge'),
(6, keras.losses.Huber(), 'Huber'),
(7, keras.losses.SquaredHinge(), 'Squared hinge'),
(8, keras.losses.LogCosh(), 'Hyperbolic Cosine'),
tb_callback = tensorboard_callback("logs", 1)
model_string = str(MODEL).split(".")
cp_callback = checkpoint_callback(str(model_string[len(model_string) - 2]), type_holder, class_holder)
save_pred_callback = SavePredCallback(batched_val_data, 'C:/Users/120392/Desktop/Training/history/final/c2onfusion_matrix')
if __name__ == '__main__':
sub = MODEL
sub.model().summary()
MODEL.compile(optimizer=tf.keras.optimizers.SGD(),
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=[tf.metrics.CategoricalAccuracy(),
keras_metrics.categorical_false_negative(),
keras_metrics.categorical_false_positive(),
keras_metrics.categorical_true_negative(),
keras_metrics.categorical_true_positive(),
keras_metrics.precision(),
keras_metrics.recall(),
])
history = MODEL.fit(
batched_training_data,
steps_per_epoch= FILE_SIZE // BATCH_SIZE, # FILE_SIZE
validation_data=batched_val_data,
validation_steps=TEST_SIZE // BATCH_SIZE, # TEST_SIZE
epochs=EPOCHS,
shuffle=True,
verbose=2,
callbacks=[cp_callback, save_pred_callback, es_callback]
)
#change name of file
