pkm_clf.py

# importing libraries 
from keras.preprocessing.image import ImageDataGenerator 
from keras.models import Sequential 
from keras.layers import Conv2D, MaxPooling2D 
from keras.layers import Activation, Dropout, Flatten, Dense 
from keras import backend as K 
  
  
img_width, img_height = 224, 224
  
train_data_dir = 'C:\\Users\\admin\\Desktop\\Python\\pokedex'
validation_data_dir = 'C:\\Users\\admin\\Desktop\\Python\\JokerCord-master\\Assets'
nb_train_samples = 400 
nb_validation_samples = 100
epochs = 10
batch_size = 16
  
if K.image_data_format() == 'channels_first': 
    input_shape = (3, img_width, img_height) 
else: 
    input_shape = (img_width, img_height, 3) 
  
model = Sequential() 
model.add(Conv2D(32, (2, 2), input_shape = input_shape)) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size =(2, 2))) 
  
model.add(Conv2D(32, (2, 2))) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size =(2, 2))) 
  
model.add(Conv2D(64, (2, 2))) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size =(2, 2))) 
  
model.add(Flatten()) 
model.add(Dense(64)) 
model.add(Activation('relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(1)) 
model.add(Activation('sigmoid')) 
  
model.compile(loss ='binary_crossentropy', 
                     optimizer ='rmsprop', 
                   metrics =['accuracy']) 
  
train_datagen = ImageDataGenerator( 
                rescale = 1. / 255, 
                 shear_range = 0.2, 
                  zoom_range = 0.2, 
            horizontal_flip = True) 
  
test_datagen = ImageDataGenerator(rescale = 1. / 255) 
  
train_generator = train_datagen.flow_from_directory(train_data_dir, 
                              target_size =(img_width, img_height), 
                     batch_size = batch_size, class_mode ='binary') 
  
validation_generator = test_datagen.flow_from_directory( 
                                    validation_data_dir, 
                   target_size =(img_width, img_height), 
          batch_size = batch_size, class_mode ='binary') 
  
model.fit_generator(train_generator, 
    steps_per_epoch = nb_train_samples // batch_size, 
    epochs = epochs, validation_data = validation_generator, 
    validation_steps = nb_validation_samples // batch_size) 
  
model.save_weights('model_saved.h5')