Hi All …
I am a new comer in deep learning applications coding. I am trying to implement a simple image classification using transfer learning. I got a code in Keras for doing the same using a transfer learning technique. Could you please provide any ideas about the pytorch equivalent of the code?
model = Sequential()
model.add(InceptionV3(include_top=False, pooling=‘max’,input_shape=(224,224,3), weights=“imagenet”))
model.add(Conv2D(32, (5, 5), input_shape=(64, 64, 3)))
model.add(Activation(‘relu’))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation(‘relu’))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation(‘relu’))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(128, activation=‘relu’))
model.add(Dense(29, activation=‘softmax’))
model.summary()