zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
training_set = train_datagen.flow_from_directory(
'C:/Users/Shubham/Desktop/MLOps-Workspace/Images/train/',
target_size=(224, 224),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'C:/Users/Shubham/Desktop/MLOps-Workspace/Images/validation/',
target_size=(224, 224),
batch_size=32,
class_mode='binary')
model.fit(
training_set,
steps_per_epoch=70,
epochs=1,
validation_data=test_set,
validation_steps=10)
# In[15]:
#Saving and loading the model
model.save('face_recog_mobilenet.h5')
from keras.models import load_model
classifier = load_model('face_recog_mobilenet.h5')
# In[26]:
model_path = os.path.join(save_dir, model_name)
weight_path = os.path.join(save_dir, weight_name)
x_train, y_train, x_test, y_test = image_crop.read_data()
x_train_resized = image_crop.resize_imgs(x_train)
y = to_categorical(y_train, num_classes=34)
model = MobileNet(include_top=True,
weights=None,
classes=34,
pooling='max',
input_shape=(150, 150, 3))
checkpoint = ModelCheckpoint(filepath=os.path.join(
save_dir, 'MobileNet_weight.{epoch:02d}-{loss:.2f}.hdf5'),
verbose=1)
opt = RMSprop(lr=2e-5)
model.compile(optimizer=opt,
loss=losses.categorical_crossentropy,
metrics=[metrics.categorical_accuracy])
model.fit(x_train_resized,
y,
epochs=10,
batch_size=36,
callbacks=[checkpoint])
model.save(model_path)
model.save_weights(weight_path)
data_test = test_datagen.flow_from_directory("DATOS/test",
target_size=(128, 128),
class_mode='binary')
model = MobileNet(input_shape=(128, 128, 3), include_top=False, pooling='avg')
x = model.output
x = Dense(512, activation="relu")(x)
predict = Dense(1, activation="sigmoid")(x)
model = Model(inputs=model.input, outputs=predict)
adam = Adam(lr=0.0001)
model.compile(optimizer=adam, loss="binary_crossentropy", metrics=["accuracy"])
model.summary()
history = model.fit(data_train,
epochs=10,
validation_data=data_test,
verbose=1)
model.save("/Clasificador.h5")
def visualizar_img_test(path, modelo):
img = cv2.imread(path)
img_1 = cv2.resize(img, (128, 128))
img = (img_1 / 255)
img = np.expand_dims(img, axis=0)
predict = modelo.predict(img)
if predict[0][0] < 0.7:
plt.title(str(100 - predict[0][0] * 100) + "% MASCARILLA")
plt.imshow(img_1.astype("uint8"))
plt.show()
# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
if not data_augmentation:
print('Not using data augmentation.')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
else:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
