TransferLearningusingVGG16/FaceRecog.py at master · raghav1674/TransferLearningusingVGG16 · GitHub

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from keras.applications import vgg16

model=vgg16.VGG16(weights="imagenet",include_top=False,input_shape=(224,224,3))

for layer in model.layers:
  layer.trainable=False

model.summary()

from keras.layers import Dense,Flatten,Dropout
def addTopModel(bottom_model, num_classes, D=256):
    """creates the top or head of the model that will be
    placed ontop of the bottom layers"""
    top_model = bottom_model.output
    top_model = Flatten(name = "flatten")(top_model)
    top_model = Dense(D, activation = "relu")(top_model)
    top_model = Dropout(0.3)(top_model)
    top_model = Dense(num_classes, activation = "softmax")(top_model)
    return top_model

model.input
model.layers

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras.models import Model

num_classes = 2

FC_Head = addTopModel(model, num_classes)

modelnew = Model(inputs=model.input, outputs=FC_Head)

print(modelnew.summary())

from keras.preprocessing.image import ImageDataGenerator

train_data_dir=r"drive/My Drive/data/train"
validation_data_dir=r"drive/My Drive/data/test"

train_datagen=ImageDataGenerator(
      rescale=1./255,
      rotation_range=20,
      width_shift_range=0.2,
      height_shift_range=0.2,
      horizontal_flip=True,
      fill_mode='nearest')

validation_datagen = ImageDataGenerator(rescale=1./255)
img_rows=224
img_cols=224

train_batchsize = 64
val_batchsize = 64

train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_rows, img_cols),
        batch_size=train_batchsize,
        class_mode='categorical')

validation_generator = validation_datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_rows, img_cols),
        batch_size=val_batchsize,
        class_mode='categorical',
        shuffle=False)

from keras.optimizers import RMSprop
from keras.callbacks import ModelCheckpoint, EarlyStopping

checkpoint = ModelCheckpoint("facemodel.h5",
                             monitor="val_loss",
                             mode="min",
                             save_best_only = True,
                             verbose=1)

earlystop = EarlyStopping(monitor = 'val_loss',
                          min_delta = 0,
                          patience = 3,
                          verbose = 1,
                          restore_best_weights = True)

# we put our call backs into a callback list
callbacks = [earlystop, checkpoint]

# Note we use a very small learning rate
modelnew.compile(loss = 'categorical_crossentropy',
              optimizer = RMSprop(lr = 0.001),
              metrics = ['accuracy'])

nb_train_samples = 544
nb_validation_samples = 200
epochs = 3
batch_size = 64

history = modelnew.fit_generator(
    train_generator,
    steps_per_epoch = nb_train_samples // batch_size,
    epochs = epochs,
    callbacks = callbacks,
    validation_data = validation_generator,
    validation_steps = nb_validation_samples // batch_size)

modelnew.save("facemodel.h5")

from keras.applications.vgg16 import preprocess_input
from keras.preprocessing import image
from keras.models import load_model
modelnew=load_model("family.h5")

oimg=image.load_img(r"testingfoldername",target_size=(224,224))
oimg.size

img=image.img_to_array(oimg)
import numpy as np
img.shape

img=np.expand_dims(img,axis=0)
img.shape
img=modelnew.predict(img)

# from keras.applications.vgg16 import decode_predictions
# decode_predictions(img)

img.shape
img

if img[0][0]==1 and img[0][1]==0:
  char="OBJECT1"
elif img[0][0]==0 and img[0][1]==1:
  char="OBJECT2"
else:
  char="DONT KNOW"
# from keras.applications.vgg16 import decode_predictions
# decode_predictions(img)

print(char)

image.array_to_img(oimg)

modelnew.save("family.h5")

model.summary()