This is a binary classification problem, so we will have only one neuron in the ouput layer. After convolutional layers, we will utilize two fully connected layers in order to make predictions. An input to our model will be the image of size \(\). This way, we should define our layers in _init_ and implement the model’s forward pass in call. We will build our model by using the “model subclassing technique”. Let’s move onto loading the necessary libraries that we will need for this tutorial. Once this is completed, we will move on to unzipping and creating a path location for the training and validation set. We will start by downloading the dataset from the online repository. In order to demonstrate this, we will create a convolutional neural network in TensorFlow and train it on Cats-vs-Dogs dataset.įirst, we will prepare our dataset for training. This method will be very beneficial when we do not have enough data at our disposal.Ī familiar question is “why should we use data augmentation?”. Highlights: In this post we will show the benefits of data augmentation techniques as a way to improve performance of a model.
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