![]() ![]() ![]() _getitem_: extracting an item from dataset. ![]() _len_: returning the legth of the dataset._init_: initializing the dataset / variables.The significant thing is the Sequence could be extended, and it must implement three methods: This object is handled for fitting to a sequence of data like dataset. Ok, then I must split the data in some way using the Sequence class. However, this throws an error about the capacity to store data into the graphics card's memory □. The habitual way to train the network using Tensorflow is as follows: model.fit(x=x_train, y=y_train) Just for simplicity, I assumed that train and validation data have the following shapes: # x_train.shape is (800, 64, 64, 1) Notice that training data and validation data are stored into the NumPy arrays (x_train, y_train) and (x_val, y_val) respectively. That function creates the models using the layer structure for your CNN architecture (e.g. X_train, y_train = load_data(source, 0.8)Īlso, the CNN model was constructed and compiled using a function called get_model. In this case, the splitting is 80% for training, and 20% for validation is made. The data is loaded into the source object, and the function load_data splits that data into training and validation. Images have a size of 64圆4 pixels with one single channel. Only focus on the training stage, only considering training and validation data, and ignoring the test/evaluation data. Now, let me explain my solution using the Sequence object □ Solution I forgot to mention that this post is coded for Tensorflow 2 using Python 3. Then, I discovered the Sequence object in Tensorflow. Sometimes, these blocks are usually known as batches or chunks. However, that is not an option for me.Īnother option is handling how to load the data into a limited memory of a graphics card: using small blocks of memory that fit into the memory to feed the network right away. One possible solution is to buy a new graphics card! □. Then, my poor Nvidia GeForce GTX 1070 with 8GB of RAM is not enough to load the dataset □. First, let me explain the context: I executed a convolutional neural network (CNN) using 2D grayscale images (extracted from MRIs). ![]()
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