The augmented model can be compiled using the ‘compile’ method, which also takes ‘SparseCategoricalCrossentropy’ as parameter to calculate the loss associated with training.
We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.
A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
Due to overfitting, the model will not be able to generalize well on the new dataset. There are many ways in which overfitting can be avoided. We can use drop out technique to overcome overfitting. Overfitting can be reduced by introducing dropout in the network. This is considered as a form of regularization. This helps expose the model to more aspects of the data, thereby helping the model generalize better.
When dropout is applied to a layer, it randomly drops out a number of output units from the layer when the training is going on. This is done by setting the activation function to 0. Dropout technique takes a fractional number as the input value (like 0.1, 0.2, 0.4, and so on). This number 0.1 or 0.2 basically indicates that 10 percent or 20 percent of the output units are randomly from the applied layer.
Data augmentation generates additional training data from the existing examples by augmenting them with the help of random transformations that would yield believable-looking images. Following is an example:
print("Compiling the model") model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) print("The complete architecture of the model") model.summary()
Compiling the model The complete architecture of the model Model: "sequential_2" Layer (type) Output Shape Param # ================================================================= sequential_1 (Sequential) (None, 180, 180, 3) 0 _________________________________________________________________ rescaling_2 (Rescaling) (None, 180, 180, 3) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 180, 180, 16) 448 _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 90, 90, 16) 0 _________________________________________________________________ conv2d_4 (Conv2D) (None, 90, 90, 32) 4640 _________________________________________________________________ max_pooling2d_4 (MaxPooling2 (None, 45, 45, 32) 0 _________________________________________________________________ conv2d_5 (Conv2D) (None, 45, 45, 64) 18496 _________________________________________________________________ max_pooling2d_5 (MaxPooling2 (None, 22, 22, 64) 0 _________________________________________________________________ dropout (Dropout) (None, 22, 22, 64) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 30976) 0 _________________________________________________________________ dense_2 (Dense) (None, 128) 3965056 _________________________________________________________________ dense_3 (Dense) (None, 5) 645 ================================================================= Total params: 3,989,285 Trainable params: 3,989,285 Non-trainable params: 0 _________________________________________________________________