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from keras.applications.vgg16 import VGG16
model = VGG16(weights='imagenet')

from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input, decode_predictions
import numpy as np

image_path = '/content/drive/My Drive/Image Datasets/random_pictures/sample_cat.jpeg'
img = image.load_img(image_path, target_size=(224, 224))

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from keras import models
from keras import layers

model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

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conv_base.trainable = True

set_trainable = False

for layer in conv_base.layers:
  if layer.name == 'block5_conv1':
    set_trainable = True
  if set_trainable:
    layer.trainable = True
  else:

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from keras.applications import VGG16

conv_base = VGG16(weights='imagenet',
                  include_top = False,
                  input_shape=(150, 150, 3))
conv_base.summary()

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from keras import layers
from keras import models

model = models.Sequential()
model.add(layers.Conv2D(96, (11, 11), strides=(4, 4), activation='relu', input_shape=(224, 224, 3)))
model.add(layers.MaxPooling2D((3, 3), strides=(2, 2))
model.add(layers.Conv2D(256, (5, 5), padding='same', activation='relu'))
model.add(layers.MaxPooling2D((3, 3), strides=(2, 2)))
model.add(layers.Conv2D(384, (3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(384, (3, 3), padding='same', activation='relu'))

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history = model.fit_generator(train_generator, steps_per_epoch=100, epochs=20, validation_data=validation_generator, validation_steps=50)

acc = history.history['accuracy'][-1]
val_acc = history.history['val_accuracy'][-1]
loss = history.history['loss'][-1]
val_loss = history.history['val_loss'][-1]

print("Training accuracy: ", acc)
print("Training loss: ", loss)

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train_datagen = ImageDataGenerator(rescale = 1./255,
                                   rotation_range = 40,
                                   width_shift_range = 0.2,
                                   height_shift_range = 0.2,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

val_datagen = ImageDataGenerator(rescale = 1./255)

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from keras.preprocessing.image import ImageDataGenerator

train_dir = os.path.join(base_dir, 'train')
test_dir = os.path.join(base_dir, 'test')
validation_dir = os.path.join(base_dir, 'validation')

train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

# class_mode: binary because we need binary labels (from using binary_crossentropy)

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from keras import layers
from keras import models

model = models.Sequential()
model.add(layers.Conv2D(32, (11, 11), activation='relu', input_shape=(224, 224, 3)))
model.add(layers.MaxPooling2D((3, 3)))
model.add(layers.Conv2D(64, (5, 5), activation='relu'))
model.add(layers.MaxPooling2D((3, 3)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.Conv2D(256, (3, 3), activation='relu'))

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import tensorflow as tf
from tensorflow.image import ResizeMethod
from keras.preprocessing import image
import numpy as np
from PIL import Image

resized_shape = (224, 224, 3)

alexnet_base_folder = '/content/drive/My Drive/Image Datasets/cats_and_dogs_alexnet'
alexnet_cats_train_dir = os.path.join(alexnet_base_folder, 'train/cats')