tf_transform_learning

from tensorflow.keras.models import Model
from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# ImageDataGenerator
train_datagen = ImageDataGenerator(
    horizontal_flip=True,
    vertical_flip=True,
    rotation_range=50,
)
batch_size = 8
train_generator = train_datagen.flow_from_directory(
    './Fruits',
    target_size=(224,224), 
    color_mode='rgb', 
    batch_size=batch_size, 
    class_mode='categorical', 
    shuffle=True)
#---number of fruits---
NO_CLASSES = max(train_generator.class_indices.values()) + 1
#---load the VGG16 model as the base model for training---
base_model = VGG16(include_top=False, input_shape=(224, 224, 3))
#---add our own layers---
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024,activation='relu')(x)       # add dense layers so
                                           # that the model can 
                                           # learn more complex 
                                           # functions and 
                                           # classify for better 
                                           # results.
x = Dense(1024,activation='relu')(x)       # dense layer 2
x = Dense(512,activation='relu')(x)        # dense layer 3
preds = Dense(NO_CLASSES,
              activation='softmax')(x)     # final layer with 
                                           # softmax activation
#---create a new model with the base model's original 
# input and the new model's output---
model = Model(inputs = base_model.input, outputs = preds)
#---don't train the first 19 layers - 0..18---
for layer in model.layers[:19]:
    layer.trainable=False
#---train the rest of the layers - 19 onwards---
for layer in model.layers[19:]:
    layer.trainable=True
        
#---compile the model---    
model.compile(optimizer='Adam',
          loss='categorical_crossentropy',
          metrics=['accuracy'])
#---train the model---
step_size_train = train_generator.n // train_generator.batch_size
model.fit(train_generator,
          steps_per_epoch=step_size_train,
          epochs=15)