Create Image-Classification-keras

Image-Classification-keras

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+# 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 = 'v_data/train'
+validation_data_dir = 'v_data/test'
+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')