def create_network():
#open up the dataset for reading and training
x = pickle.load(open("x.pickle", "rb"))
y = pickle.load(open("y.pickle", "rb"))
#noramlize the data to a fixed pixel size
x = x / 255.0
#start building the model
model = Sequential()
#create Convolutional layers (filters)
model.add(Conv2D(32, (3, 3), input_shape=x.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
#these layers are hidden
model.add(Flatten())
model.add(Dense(128))
model.add(Activation("relu"))
model.add(Dense(128))
model.add(Activation("relu"))
# the final layer needs to have the same amount of neurons as our categories
model.add(Dense(len(categories)))
model.add(Activation("softmax"))
#copmile the model
model.compile(loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
#training the model with 10 iterations (epochs)
history = model.fit(x, y, batch_size=32, epochs=25, validation_split=0.1)
#save the model
model.save_weights("model.h5")
print("Saved model to disk")
model.save('CNN.model')
return
def run2():
#Building a Deep Neural Network based classification model
model = Sequential()
model.add(
Dense(164,
input_shape=[14],
activation='relu',
kernel_regularizer='l2',
kernel_initializer='TruncatedNormal'))
model.add(Dense(164, activation='relu'))
model.add(Dense(546 * 2, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(14, activation='relu', kernel_regularizer='l2'))
model.add(BatchNormalization())
model.add(Dense(2, activation='sigmoid'))
#Save a copy of the untrained model
model.save(
'../Custom_Models/Keras_Model_H5/Untrained_Classification_Model.h5')
print("Model is saved to '/Untrained_Classification_Model.h5'")
import tensorflow
model = tensorflow.keras.models.load_model(
'../Custom_Models/Keras_Model_H5/Untrained_Classification_Model.h5')
return model
def fit(image_dir: str = IMAGE_DIR, dump: bool = True, **kwargs):
""" Read and resize images
Save all the data in:
TRAIN_X - pixels
TRAIN_Y - labels
"""
logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=logdir)
model_checkpoint = ModelCheckpoint(
str(MODELS_DIR /
'weights-improvement-{epoch:02d}-{val_accuracy:.2f}.hdf5'),
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1)
reduce_lron = ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=3,
verbose=1,
mode='auto')
base_model = InceptionV3(include_top=False,
weights='imagenet',
input_shape=(WIDHT, HEIGHT, 3))
for layer in base_model.layers:
layer.trainable = False
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dense(1024, activation='relu', kernel_regularizer=l2(0.0001)))
model.add(Dense(LABEL_SIZE, activation='softmax'))
model.compile(
loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'],
)
train_generator = augs_gen.flow_from_directory(
directory=IMAGE_DIR,
target_size=WH,
batch_size=BATCH_SIZE,
seed=1,
shuffle=True,
subset='training',
)
test_generator = augs_gen.flow_from_directory(
directory=IMAGE_DIR,
target_size=WH,
batch_size=BATCH_SIZE,
seed=1,
shuffle=True,
subset='validation',
)
labels = (train_generator.class_indices)
labels = dict((v, k) for k, v in labels.items())
with open(DATA_DIR / 'generator_labels.dump', 'wb') as file:
pickle.dump(labels, file)
with graph.as_default():
model.fit_generator(
train_generator,
validation_data=test_generator,
steps_per_epoch=train_generator.samples // BATCH_SIZE,
validation_steps=test_generator.samples // BATCH_SIZE,
epochs=EPOCHS,
verbose=1,
callbacks=[tensorboard_callback, model_checkpoint, reduce_lron],
)
print('Prepare to write data on the disk')
model.save(f'{model_name}.dump')
def read_dataset(image_dir: str = IMAGE_DIR, dump: bool = True, **kwargs):
""" Read and resize images
Save all the data in:
TRAIN_X - pixels
TRAIN_Y - labels
"""
logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=logdir)
base_model = InceptionV3(include_top=False,
weights='imagenet',
input_shape=(WIDHT, HEIGHT, 3))
for layer in base_model.layers:
layer.trainable = False
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dense(1024, activation='relu'))
model.add(Dense(LABEL_SIZE, activation='softmax'))
model.compile(
loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'],
)
# def define_label(parent_name):
# return "-".join(parent_name.split('-')[1:])
# count = 0
# for subdir, dirs, files in os.walk(image_dir):
# print(f'PATH: {subdir} is processing')
# count += 1
# for file in files:
# path = pathlib.Path(subdir).absolute() / file
# image = load_img(str(path), target_size=WH)
# TRAIN_X.append(np.array(image))
# image_label = define_label(path.parent.name)
# TRAIN_Y.append(image_label)
# label_encoder = LabelEncoder()
# TRAIN_Y = label_encoder.fit_transform(TRAIN_Y)
# TRAIN_Y = np.array(to_categorical(TRAIN_Y, num_classes=LABEL_SIZE))
# x_train, x_test, y_train, y_test = train_test_split(
# np.array(TRAIN_X),
# TRAIN_Y,
# test_size=0.2,
# random_state=69,
# )
train_generator = augs_gen.flow_from_directory(
directory=IMAGE_DIR,
target_size=WH,
batch_size=BATCH_SIZE,
seed=1,
shuffle=True,
subset='training',
)
test_generator = augs_gen.flow_from_directory(
directory=IMAGE_DIR,
target_size=WH,
batch_size=BATCH_SIZE,
seed=1,
shuffle=True,
subset='validation',
)
labels = (train_generator.class_indices)
labels = dict((v, k) for k, v in labels.items())
with open(DATA_DIR / 'generator_labels.dump', 'wb') as file:
pickle.dump(labels, file)
model.fit_generator(
train_generator,
validation_data=test_generator,
steps_per_epoch=train_generator.samples // BATCH_SIZE,
validation_steps=test_generator.samples // BATCH_SIZE,
epochs=EPOCHS,
verbose=1,
callbacks=[tensorboard_callback],
)
print('Prepare to write data on the disk')
model.save(f'{model_name}_without_iter.dump')
def read_dataset(image_dir: str = IMAGE_DIR, dump: bool = True, **kwargs):
""" Read and resize images
Save all the data in:
TRAIN_X - pixels
TRAIN_Y - labels
"""
global TRAIN_X, TRAIN_Y
logdir = "logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=logdir)
base_model = InceptionV3(include_top=False,
weights='imagenet',
input_shape=(WIDHT, HEIGHT, 3))
for layer in base_model.layers:
layer.trainable = False
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
# model.add(Dense(512, activation='relu'))
model.add(Dense(LABEL_SIZE, activation='softmax'))
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'],
)
def define_label(parent_name):
return "-".join(parent_name.split('-')[1:])
for subdir, dirs, files in os.walk(image_dir):
for file in files:
path = pathlib.Path(subdir).absolute() / file
image_label = define_label(path.parent.name)
TRAIN_Y.append(image_label)
label_encoder = LabelEncoder()
TRAIN_Y = label_encoder.fit_transform(TRAIN_Y)
TRAIN_Y = np.array(to_categorical(TRAIN_Y, num_classes=LABEL_SIZE))
count = 0
current_length_train_x = 0
for subdir, dirs, files in os.walk(image_dir):
print(f'PATH: {subdir} is processing')
count += 1
for file in files:
path = pathlib.Path(subdir).absolute() / file
image = load_img(str(path), target_size=WH)
TRAIN_X.append(np.array(image))
if count % 40 == 0:
slice_left = current_length_train_x
slice_right = slice_left + len(TRAIN_X)
current_length_train_x = slice_right
# convert to binary matrix (120 labels at all) 2^10 = 128
# normalize image
# split image
# TODO: make active on resume iterations
# if count == 40:
# # make empty
# TRAIN_X = []
# model = load_model(f'{model_name}_iter_40.dump')
# continue
x_train, x_test, y_train, y_test = train_test_split(
np.array(TRAIN_X),
TRAIN_Y[slice_left:slice_right],
test_size=0.2,
random_state=69,
)
# make empty
TRAIN_X = []
augs_gen.fit(x_train)
model.fit_generator(
augs_gen.flow(x_train, y_train, batch_size=25),
validation_data=(x_test, y_test),
validation_steps=1000,
steps_per_epoch=1000,
epochs=20,
verbose=1,
callbacks=[tensorboard_callback],
)
del x_train, x_test, y_train, y_test
model.save(f'{model_name}_iter_{count}.dump')
print(f'Executed {count} / 121')
print('Prepare to write data on the disk')