I don't understand how images are actually fed into a CNN? If I have a directory containing a few thousand images, what steps do I need to take in order to feed them to a neural network (for instance resizing, grey scale, labeling, etc) I don't understand how even the labeling of an image works. What would this dataset actually look like? Or can you not look at it at all (in a summarized form, I'm thinking something like a table)?

Dataset just consists of Features and Labels. Here features are your images and labels are the classes.

There is a fit() method for every CNN model, which will take in Features and Labels, and performs training.

for the first layer, you need to mention the input dimension of image, and the output layer should be a softmax (if you're doing classification) with dimension as the number of classes you have.

model = Sequential()

model.add(Conv2D(32, (3, 3), padding='same', input_shape=(64, 64, 3)))

model.add(Activation('relu'))

model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))



model.add(Flatten())

model.add(Dense(512))

model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(num_classes))

model.add(Activation('softmax'))



model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])



model.fit(x_train, y_train, epochs=10)

The above is the code for training a Keras sequenctioal model.

General Points:

1. input_shape should be the dimension of X_train.


2. You need to get this shape when you do X_train.shape (numpy)


3. Convolutions are then applied with respective Activations


4. Dropout and Pooling layers are optional.


5. After the convolution layers, the data is flattened. using Flatten()


6. Then it is sent to few Fully Connected layers


7. The last but one layer should have the dimensions of number of classes


8. Last layer will be softmax.


9. Now, compile the model with the loss, optimizer and metric


10. Then fit()

Vote up ;) if you like it.

This is a very packed question. Let's try to go through it and I will try to provide some example for image processing using a CNN.

How to structure the data?

The shape of the variable which you will use as the input for your CNN will depend on the package you choose. I prefer using tensorflow, which is developed by Google. If you are planning on using a pretty standard architecture, then there is a very useful wrapper library named Keras which will help make designing and training a CNN very easy.

When using tensorflow you will want to get your set of images into a numpy matrix. The first dimension is your instances, then your image dimensions and finally the last dimension is for channels.

So for example if you are using MNIST data as shown below, then you are working with greyscale images which each have dimensions 28 by 28. Then the numpy matrix shape that you would feed into your deep learning model would be (n, 28, 28, 1), where $n$ is the number of images you have in your dataset.