# What is input shape in CNN?

## What is input shape in CNN?

Input Shape You always have to give a 4 D array as input to the CNN . So input data has a shape of (batch_size, height, width, depth), where the first dimension represents the batch size of the image and the other three dimensions represent dimensions of the image which are height, width, and depth.

## What is the output of Conv1D?

so likewise conv1d will take all 32 input channels one by one and generate 32 different output channel for each input channels, so in total 32×32=1024 channels output should be generated, but Conv1d output is only 32 channel output is generated instead of 1024 channels.

## What is Conv1D?

We can see that the 2D in Conv2D means each channel in the input and filter is 2 dimensional(as we see in the gif example) and 1D in Conv1D means each channel in the input and filter is 1 dimensional(as we see in the cat and dog NLP example).

## What is filter and kernel size in Conv1D?

filters: Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution). kernel_size: An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.

## Is kernel size same as filter size?

In a given convolution layer, the Kernel size is the X * Y dimensions, and the number of filters (or “channels” as it’s often called) is the Z dimension. The Kernel size usually defines a relatively small square consisting of X*Y numbers that together encode a specific feature / pattern.

## What is a good kernel size?

A common choice is to keep the kernel size at 3×3 or 5×5. The first convolutional layer is often kept larger. Its size is less important as there is only one first layer, and it has fewer input channels: 3, 1 by color.

## What is difference between kernel and filter in CNN?

Kernel vs Filter For example, in 2D convolutions, the kernel matrix is a 2D matrix. A filter however is a concatenation of multiple kernels, each kernel assigned to a particular channel of the input. So for a CNN layer with kernel dimensions h*w and input channels k, the filter dimensions are k*h*w.

## Why is smaller kernel size more meaningful?

So, with smaller kernel sizes, we get lower number of weights and more number of layers. So, with larger kernel sizes, we get a higher number of weights but lower number of layers. Due to the lower number of weights, this is computationally efficient.

## What is the difference between kernel and filter?

A “Kernel” refers to a 2D array of weights. The term “filter” is for 3D structures of multiple kernels stacked together. For a 2D filter, filter is same as kernel. But for a 3D filter and most convolutions in deep learning, a filter is a collection of kernels.

## What is the filter in CNN?

In CNNs, filters are not defined. The value of each filter is learned during the training process. This also allows CNNs to perform hierarchical feature learning; which is how our brains are thought to identify objects. In the image, we can see how the different filters in each CNN layer interprets the number 0.

## Why does CNN use kernel?

The kernel is a matrix that moves over the input data, performs the dot product with the sub-region of input data, and gets the output as the matrix of dot products. Kernel moves on the input data by the stride value. In short, the kernel is used to extract high-level features like edges from the image.

## Why does CNN use ReLU?

The ReLU function is another non-linear activation function that has gained popularity in the deep learning domain. ReLU stands for Rectified Linear Unit. The main advantage of using the ReLU function over other activation functions is that it does not activate all the neurons at the same time.

## What does dropout layer do in CNN?

Dropout is a technique used to prevent a model from overfitting. Dropout works by randomly setting the outgoing edges of hidden units (neurons that make up hidden layers) to 0 at each update of the training phase.

## What is flatten layer in CNN?

Flatten is the function that converts the pooled feature map to a single column that is passed to the fully connected layer. Dense adds the fully connected layer to the neural network.

## Why is leaky ReLU better than ReLU?

Leaky ReLU & Parametric ReLU (PReLU) Leaky ReLU has two benefits: It fixes the “dying ReLU” problem, as it doesn’t have zero-slope parts. It speeds up training. There is evidence that having the “mean activation” be close to 0 makes training faster.

## What does ReLU stand for?

The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero.

## Which is better ReLU or LeakyReLU?

In my experience, LeakyReLU shows at least the same or better results in most comparisons with ReLU, but moreover, it allows NN to learn in setups (architectures) where the ReLU fails. For example, it’s the case where a NN architecture contains “bottlenecks” – very narrow layers with small neurons count.

## What is leaky ReLU activation and why is it used?

Leaky ReLU function is an improved version of the ReLU activation function. As for the ReLU activation function, the gradient is 0 for all the values of inputs that are less than zero, which would deactivate the neurons in that region and may cause dying ReLU problem. Leaky ReLU is defined to address this problem.

## What is output of ReLU?

According to equation 1, the output of ReLu is the maximum value between zero and the input value. An output is equal to zero when the input value is negative and the input value when the input is positive.

## Is ReLU good for classification?

No, it does not. For binary classification you want to obtain binary output: 0 or 1. To ease the optimization problem (there are other reason to do that), this output is subtituted by the probability of been of class 1 (value in range 0 to 1). If you use relu you will obtain a value from 0 to inf as output.

## What will happen if the learning rate is set too low or too high?

If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function.

## Does learning rate affect accuracy?

Learning rate is a hyper-parameter th a t controls how much we are adjusting the weights of our network with respect the loss gradient. Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy).

## What happens when learning rate is too big?

A learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck. The learning rate is perhaps the most important hyperparameter. If you have time to tune only one hyperparameter, tune the learning rate.

## What is the default learning rate for Adam?

optimizers. schedules. LearningRateSchedule , or a callable that takes no arguments and returns the actual value to use, The learning rate. Defaults to 0.001.

## How does Adam Optimizer work?

Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems.

## Does Adam Optimizer change learning rate?

5 Answers. It depends. ADAM updates any parameter with an individual learning rate. This means that every parameter in the network has a specific learning rate associated.

## What is the default learning rate?

A traditional default value for the learning rate is 0.1 or 0.01, and this may represent a good starting point on your problem.

## Is RMSProp better than Adam?

So far, we’ve seen RMSProp and Momentum take contrasting approaches. While momentum accelerates our search in direction of minima, RMSProp impedes our search in direction of oscillations. Adam or Adaptive Moment Optimization algorithms combines the heuristics of both Momentum and RMSProp.