Week 11 part 1 &2, Week 12 Part 1&2, Week 13, Week 14 part 1 &2

Overall autoencoders?

are powerful tools for learning data representations and have various applications across many domains in machine learning.

Ganglioglioma:

A rare tumor that forms from a mixture of neuron and glial cells in the central nervous system.

Steps of the run experiment ?

1. Defines a file path (filepath) to save model checkpoints during training. 2. Configures a model checkpoint callback to save the best model weights during training. 3. Creates an instance of the sequence model using get_sequence_model(). 4. Fits the model to the training data (train_data[0] for frame features and train_data[1] for frame masks) with training labels (train_labels). It uses a validation split of 30% and trains for a specified number of epochs (EPOCHS). 5. After training, it loads the best model weights from the saved checkpoints using seq_model.load_weights(filepath). 6. Evaluates the model's accuracy on the test data (test_data[0] for frame features and test_data[1] for frame masks) with test labels (test_labels). 7. Finally, it prints the test accuracy, rounding it to two decimal places.

Carcinoma:

A cancer that starts in the skin or the tissues that line other organs.

Medulloblastoma:

A cancerous brain tumor that starts in the lower back part of the brain and is more common in children.

Neurocytoma:

A rare brain tumor that arises from neuronal cells and is often found in the ventricles of the brain.

Granuloma

A small area of inflammation caused by infection or inflammation, not necessarily cancerous.

Ependymoma:

A tumor that arises from the ependymal cells that line the passageways where cerebrospinal fluid flows in the brain.

Oligodendroglioma

A tumor that occurs in the brain cells that produce the myelin sheath which insulates nerve cells.

Germinoma

A type of cancer that typically arises in the brain or ovaries/testes, originating from germ cells.

Meningioma:

A typically benign tumor that forms from the membranes covering the brain and spinal cord.

Glioblastoma:

A very aggressive type of brain tumor that comes from supportive brain tissue called glial cells.

In order to combat vanishing gradients what are some of the several strategies that researchers develop?

Activation Functions: Use activation functions that are less prone to vanishing gradients, like ReLU (Rectified Linear Unit) and its variants (Leaky ReLU, Parametric ReLU, etc.), which do not saturate in the same way as sigmoid or tanh. Network Initialization: Careful initialization of network weights can help prevent vanishing gradients. Xavier and He initialization are strategies designed to keep the gradients in a reasonable range during the beginning of training. Batch Normalization: Normalizing the inputs of each layer to have a mean of zero and a variance of one can prevent gradients from becoming too small. Residual Networks (ResNets): These networks introduce "shortcut connections" which allow gradients to bypass some layers and mitigate the vanishing gradient problem. Gradient Clipping: Involves scaling the gradients when they exceed a certain threshold to prevent them from getting too small (or too large, addressing the related problem of exploding gradients).

How are the images separated as?

By astrocytoma, carcinoma, ependymoma, ganglioglioma, germinoma, glioblastoma, granuloma, medulloblastoma, meningioma, neurocytoma, oligodendroglioma, papilloma, schwannoma and tuberculoma.

What is GRU?

Aother RNN variant, also overcomes the vanishing gradient problem. It employs a simpler architecture than LSTM, with fewer gates, which can make it computationally less intensive. G RUs maintain a balance between capturing long-range dependencies and computational efficiency, making them an excellent choice for many sequence-related tasks.

Recurrent Neural Networks (RNNs)

Are a class of artificial neural networks uniquely suited for sequential data analysis. Unlike standard feedforward neural networks, which process data in a single pass, RNNs are designed to capture and analyze sequences of data. This makes them invaluable for tasks involving time series data, natural language processing, speech recognition, and more.

What is Convolutional Neural Networks (CNNs) ?

Are excellent at capturing spatial features in images, making them ideal for tasks like object detection and recognition.

What is Recurrent Neural Networks (RNNs)?

Are specialized for sequential data and can model temporal dependencies, which are crucial in understanding the dynamics of videos.

How have LSTM networks become popular choice for sequence modeling tasks?

Due to their ability to mitigate the vanishing gradient problem, making them effective at modeling complex patterns within data.

Why Do Gradients "Vanish"?

As the gradient of the loss is backpropagated to earlier layers, repeated multiplication may cause the gradient to become exponentially smaller. This is especially true when the gradients are fractions (between 0 and 1), which occurs frequently with certain activation functions like the sigmoid or tanh. Since each layer's update is proportional to the gradient, when this gradient becomes infinitesimally small, the weights of the initial layers barely update. This means that these layers learn very slowly, if at all, which can significantly impede the overall learning process of the network.

What are some forms of brain tumors?

Astrocytoma: A type of brain tumor that develops from star-shaped cells in the brain called astrocytes.

Applications of CNN & RNN

CNN-RNN architectures have a wide range of applications, including video captioning, anomaly detection, surveillance, and sports analysis. They can describe the content of videos, predict future frames, and identify unusual events in surveillance footage.

What is functionality ?

CPUs are designed to handle a wide range of tasks but are limited in handling multiple tasks simultaneously. GPUs are specialized for compute-intensive, highly parallel computations - hence they are preferred for tasks that require processing large blocks of data, like 3D graphics rendering or complex scientific calculations.

Use Cases

CPUs are versatile and can run all types of programs, but they excel in tasks that require complex decision-making and I/O operations. GPUs are used when computation can be parallelized, such as graphics and video rendering, and more recently in accelerating certain types of computation in data science and AI.

Architecture:

CPUs consist of a few cores optimized for sequential processing with higher clock speeds, while GPUs comprise thousands of smaller, more efficient cores designed for multitasking across large datasets.

Memory Hierarchy

CPUs typically have a more complex and sophisticated memory hierarchy, including various levels of cache, which helps in faster access to frequently used data. GPUs have a simpler memory hierarchy but generally more overall memory bandwidth to accommodate the high amount of parallelism.

label_processor = keras.layers.StringLookup(...):

Creates a StringLookup layer, which is part of the Keras layers module. The StringLookup layer is used for mapping strings (in this case, labels or tags) to integers. It can be helpful in text classification and natural language processing tasks.

feature_extractor = build_feature_extractor():

Creates a feature extractor model based on InceptionV3 and returns the model. You assign the returned model to the variable feature_extractor. This variable can now be used to extract features from images.

build_feature_extractor()

Creates a feature extractor using the InceptionV3 model

What are modern CPU composed of?

Composed of several cores, which can handle separate tasks, allowing for more efficient processing of multiple tasks simultaneously (multithreading). This is particularly useful for general-purpose computations and tasks that require sequential processing.

## Lab 11 What are two fundamental building blocks of deep learning that creates video analysis?

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

What are the applications of Autoenoders?

Dimensionality Reduction: Similar to PCA, autoencoders can reduce the dimensionality of the data by learning a compressed representation. Denoising: Autoencoders can be used to remove noise from data by learning to ignore the "noise" in the input data and reconstruct the "clean" data. Feature Learning: They can learn to encode input data into meaningful features which can then be used for various tasks such as classification. Anomaly Detection: Since autoencoders learn to reproduce the normal data, they can be used to detect anomalies by identifying data points that have a high reconstruction error.

What is training?

During training, autoencoders are given unlabeled data as input which is then passed through the encoder to create the code. The decoder then takes this code and attempts to recreate the input data. The difference between the original input and the reconstructed output is calculated using a loss function (like mean squared error), and the parameters of the model are updated to minimize this reconstruction loss. The learning process is unsupervised because we're not telling the model what features to look for; instead, it learns on its own to represent the data in a way that can be reconstructed.

include_top=False

Excludes the final classification layer (top layer) of the InceptionV3 model, as it's used as a feature extractor and not for classification.

How Do Exploding Gradients Happen?

Exploding gradients are typically the result of very high error gradients accumulating during backpropagation through the network. This can be due to: 1. Deep networks with many layers: Each layer amplifies the gradients as they backpropagate. 2. Use of certain activation functions: For example, ReLU activation function does not saturate for high input values. 3. Large initial weights: If the weights are initialized with very high values, the gradients can be amplified as they pass through.

What are Exploding Gradients?

Exploding gradients occur when the gradients during training become very large, so large in fact that they cause the learning process to become unstable. This instability is due to the weights in the network receiving updates that are so large that they "explode," which causes the model to diverge and the loss function to become NaN (not a number) or Inf (infinity), indicating that the model is broken and cannot learn from the data.

What are CPU & GPUs both essential for?

For the functioning of a computer

What are some differences Between CPU and GPU:

Functionality Architecture Memory Hierarchy Use Cases:

How to Mitigate Exploding Gradients:

Gradient Clipping Weight Initialization Batch Normalization Changing the Architecture Using LSTM/GRU for RNNs

What are some features of CPU?

Has Low Compute Density Complex control logic Large Caches Optimized for serial operations Shallow Pipelines Low latency tolerance

Raw Computional Power of GPU?

Has been harnessed for more than just graphics rendering. With the advent of General-Purpose computing on Graphics Processing Units (GPGPU), GPUs are increasingly used for scientific computing and machine learning tasks that can benefit from parallel processing, like matrix and vector operations.

What is Using LSTM/GRU for RNNs?

In the case of recurrent neural networks, using Long Short-Term Memory (LSTM) units or Gated Recurrent Units (GRU), which are designed to avoid exploding gradients, can be helpful.

vocabulary=np.unique(train_df["tag"])

This parameter defines the vocabulary for the StringLookup layer. It's set to the unique values found in the "tag" column of the train_df DataFrame. The vocabulary is the set of all unique labels you want to map to integers.

feature_extractor = keras.applications.InceptionV3(...)

In this line, an instance of the InceptionV3 model is created using the Keras library

CNNs in Video Analysis

In video analysis, CNNs are often used to extract spatial information from individual frames of the video. These frames are treated as images, and the CNN layers process them to detect objects, patterns, or features within each frame. The resulting feature maps capture essential spatial information.

What is autoencoder?

Is a type of artificial neural network used to learn efficient representations of data, typically for the purpose of dimensionality reduction or feature learning. These representations are learned unsupervised, which means they don't require labeled input/output pairs. The main components of an autoencoder are the encoder, the code, and the decoder.

StringLookup layer

Is often used as part of text classification models where text data (labels or tags) needs to be converted into numerical form for model training. It provides a convenient way to encode text labels into integers for deep learning models to process.

What is Graphics Processing Unit (GPU)?

Is specialized hardware designed to accelerate the rendering of images, animations, and video for the computer's display. GPUs are highly efficient at manipulating computer graphics and image processing with their parallel structure Are much more efficient than general-purpose CPUs for algorithms that process large blocks of data in parallel.

What is a Gradient in Neural Networks?

Is to find the minimum of a function — typically a loss or cost function that measures how wrong our model's predictions are. The gradient represents the slope of this function at a particular point, and it tells us in which direction we should adjust our parameters (weights and biases) to reduce the error.

num_oov_indices=0:

This parameter specifies the number of out-of-vocabulary (OOV) indices. In this case, it's set to 0, meaning that there are no OOV indices. OOV indices are used for handling tokens (labels) that are not present in the vocabulary.

About LSTM & GRU networks

LSTM networks and GRU networks, both types of RNNs, enhance the modeling of temporal dependencies, with LSTMs offering a more complex solution and GRUs offering a more computationally efficient alternative. These advancements in RNN architecture have significantly improved their effectiveness in a range of real-world applications.

By using both CPUs and GPUs in tandem

Modern computers and applications can achieve optimal performance by assigning tasks to the processor type most suitable for each task. The CPU handles the general operation and orchestrates the GPU for tasks that require its specialized processing power.

What is Batch Normalization?

Normalizing the inputs of each layer to have a mean of zero and a variance of one helps to control the distribution of activations, thereby preventing gradients from becoming too large.

## Week 12 Central Processing Unit (CPU)

Often described as the brain of the computer. It is responsible for carrying out most of the instructions from computer programs through basic arithmetic, logic, control, and input/output (I/O) operations specified by the instructions. The CPU performs essential tasks such as running the operating system, applications, and user commands.

What are some challenges of CNN & RNN?

One challenge in building CNN-RNN models for video analysis is the need for extensive computational resources, as videos can be quite large and require substantial processing. Additionally, handling long videos with complex scenes can be challenging, and the choice of hyperparameters, such as network depth and sequence length, is critical for optimal performance

## Week 12 - 13 In week 12 and 14 what does the datasets consist of?

Private collection go T1, contrast enhanced T1, T2 Magnetic resonance images separated by brain tumor type.

RNNs in Video Analysis

RNNs play a vital role in understanding the temporal aspects of videos. RNNs can model the relationships between frames by considering the order and timing of their occurrence. This helps in tasks like action recognition, video captioning, and predicting future frames.

Exploding Gradient

Shows the opposite issue where gradients become excessively large. This can happen due to the multiplication of gradients that are greater than 1 through many layers. When gradients explode, they can cause learning to diverge, often resulting in numerical instability and a failure to converge to a solution.

test_data, test_labels = prepare_all_videos(test_df, "/content/ucf101_top5/ucf101_top5/test/"):

Similarly, this line prepares the test data by calling the prepare_all_videos function with the test DataFrame (test_df) and the root directory path for test videos.

What is Changing the Architecture?

Sometimes, altering the network architecture by reducing depth or altering the connections (like using skip connections as in ResNets) can mitigate the issue.

pooling="avg

Specifies that the global average pooling layer should be added at the end of the model. This layer computes the average value for each feature map.

weights="imagenet"

Specifies that the pretrained ImageNet weights should be used

input_shape=(IMG_SIZE, IMG_SIZE, 3):

Specifies the shape of the input images, which should be IMG_SIZE pixels in height and width, with 3 color channels (RGB).

RNNS role in Neural Networks ?

Tailored for sequential data analysis. They're widely applied in various domains where the understanding of sequences is crucial.

What is Decoder?

The decoder part of the network aims to reconstruct the input data from the code. The decoder takes the encoded data and expands it back to the original data dimension. The output of the decoder should ideally be very similar to the input of the encoder.

What is encoder?

The encoder's role is to compress the input into a latent-space representation. It encodes inputs into a lower-dimensional space which is a compressed representation of the input data. The encoder portion is typically a network itself that transforms the input data into a smaller, dense representation, which is the "code."

What are some of the consequences of Exploding gradients?

The model may fail to converge or even to start learning. The weight updates may be so large that they could overshoot any meaningful solution. The model's parameters may become NaN or infinity, meaning that any subsequent training is effectively meaningless.

What are some of the limitations of traditional RNNs?

The most notable challenge is the "vanishing gradient" problem, which can hinder learning when sequences are long. To address these issues, more advanced variants of RNNs were developed, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

What is Variations?

There are several variations of autoencoders, each with specific properties that can be advantageous in different scenarios: Sparse Autoencoder: Imposes sparsity on the hidden layers to learn more robust features. Denoising Autoencoder: Intentionally corrupts input data with noise and learns to recover the original undistorted data. Variational Autoencoder (VAE): A probabilistic spin on autoencoders that not only learns a representation but also the parameters of a probability distribution representing the data. Convolutional Autoencoder: Uses convolutional layers to encode image data, which is particularly effective for tasks involving image data.

preprocessed = preprocess_input(inputs):

The preprocess_input function is applied to the input tensor, preprocessing the input images. This ensures that the input images are transformed in a way compatible with the InceptionV3 model's expectations.

outputs = feature_extractor(preprocessed)

The preprocessed input is passed through the feature extractor (InceptionV3) to obtain the feature vectors from the images.

What Does "Vanishing" Mean?

The term "vanishing" refers to the tendency of these gradients to become very small, effectively approaching zero as the training process progresses. This happens predominantly in networks with many layers (deep neural networks).

Impact on Deep Learning

The vanishing gradient problem is particularly troublesome for deep networks because the gradients have to go through many layers of transformations. The deeper the network, the worse the problem can get. Early layers often learn features associated with the raw data, like edges in images, and if they stop learning, the entire network's performance can plateau or not improve at all.

test_video = "v_PlayingCello_g07_c02.avi"

This line specifies the path to a test video (you can change it to any video path you want to test).

test_frames = sequence_prediction(test_video)

This line uses the sequence_prediction function to predict the sequence of actions for the specified test video. It also returns the frames of the video.

print(f"Frame features in train set: {train_data[0].shape}") and print(f"Frame masks in train set: {train_data[1].shape}"):

These lines print the shapes of the frame features and frame masks in the training set, providing you with information about the dimensions of the data that will be fed into the sequence model.

import warnings:

This allows you to control how warnings are displayed or filter them, providing more control over the warning behavior in your code.

return keras.Model(inputs, outputs, name="feature_extractor")

This function creates and returns a Keras Model with the specified inputs and outputs. The model is named "feature_extractor" and is essentially a part of the InceptionV3 model up to the global average pooling layer. It will be used for feature extraction from input images.

get_sequence_model():

This function defines a sequence model for video classification. It creates a model that takes frame features and frame masks as input and produces predictions as output. The model architecture includes two GRU layers, a dropout layer, and two dense layers. The model is compiled with the sparse categorical cross-entropy loss function and the Adam optimizer.

load_video(path, max_frames=0, resize=(IMG_SIZE, IMG_SIZE)):

This function is used to load a video from a file specified by path. It reads the video frame by frame, processes each frame using the crop_center_square function, resizes it to a specified size, and converts it to a format suitable for further processing.

sequence_prediction(path):

This function performs sequence prediction for a given video. It takes the path to a video as input (path). It does the following: 1. Loads the frames of the video located at the specified path using the load_video function. 2. Calls prepare_single_video(frames) to prepare the frames for sequence prediction. It obtains the frame features and frame masks. 3. Uses the sequence_model to predict the sequence of actions or labels for the video based on the prepared frame features and masks. 4. Prints the predicted labels and their corresponding probabilities in descending order. Returns the frames of the video.

Prepare_single_video(frames):

This function prepares a single video for sequence prediction. It takes a list of frames (frames) as input. It processes the frames, extracts features, and creates frame masks for the video. The function returns the frame features and frame masks.

run_experiment()

This function runs an experiment using the defined sequence model.

prepare_all_videos(df, root_dir)

This function takes a DataFrame (df) and a root directory path (root_dir) as input and prepares video data for training. It performs the following tasks for each video in the DataFrame: Extracts the video paths and labels from the DataFrame. Converts the labels into numerical format using the label_processor. Initializes arrays to store the masks and features for each video.

to_gif(images)

This function takes a list of images (images) and converts them into a GIF format. It saves the GIF as "animation.gif" and returns an embeddable link to the GIF.

crop_center_square(frame)

This function takes an input video frame (an image) and crops it to a square shape by selecting the central region of the frame. It calculates the dimensions of the square by finding the minimum dimension (either width or height) of the frame and then centers the square within the frame. It returns the cropped square frame.

What is Gradient clipping?

This involves scaling down the gradients when they exceed a certain threshold, thus preventing them from growing too large.

What is Code?

This is the representation of the compressed data provided by the encoder. It is a lower-dimensional space, often referred to as the latent space or bottleneck. This code is what the autoencoder uses to reconstruct the input data.

preprocess_input = keras.applications.inception_v3.preprocess_input:

This line assigns the preprocessing function preprocess_input to a variable. This function is used to preprocess input images before feeding them into the InceptionV3 model.

inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3)):

This line creates a Keras input tensor for the feature extractor model. It specifies the shape of the input images, matching the previously defined IMG_SIZE and the fact that there are 3 color channels (RGB).

_, sequence_model = run_experiment()

This line of code runs the experiment by calling the run_experiment() function and assigns the history (training history) to _ (which is not used) and the trained sequence model to sequence_model.

train_data, train_labels = prepare_all_videos(train_df, "/content/ucf101_top5/ucf101_top5/train/"):

This line prepares the training data by calling the prepare_all_videos function with the training DataFrame (train_df) and the root directory path where training videos are located.

print(label_processor.get_vocabulary())

This line prints the vocabulary that the label_processor has learned from the "tag" column of your training data. It will display the unique labels (tags) that the model can map to integers.

CNN-RNN Fusion

To build a CNN-RNN architecture, the feature maps extracted by CNNs are often fed into RNNs as input. This combination allows the model to simultaneously capture spatial and temporal information from videos. For example, in action recognition, CNNs can identify the pose of a person in each frame, and RNNs can analyze the sequence of these poses to determine the performed action.

Vanishing Gradient"

Used for updating the weights in the network through backpropagation Demonstrates how the gradient can get smaller and smaller as it is propagated back through the layers. When the gradients become very small, they do not contribute much information for learning in the earlier layers, hence the term "vanishing." This means that the weights in the initial layers of the network hardly change, leading to very slow or stalled learning for these layers.

what is weight Initialization?

Using a proper method for initializing weights, like He initialization for ReLU networks, can help prevent the early onset of exploding gradients.

What is LSTM networks ?

is a type of RNN, have an intricate internal structure featuring gates that control the flow of information into and out of a memory cell. This architectural design empowers LSTMs to capture and remember long-range dependencies in sequences.

What is the defining characteristic of RNNs?

is their ability to maintain a hidden state that serves as a form of memory, preserving information from previous time steps while processing current inputs. This recurrent nature empowers RNNs to model temporal dependencies within sequences.

In summary exploding gradients are?

significant issue in training deep neural networks and can prevent a model from learning effectively. It's a problem that's more likely to arise in deeper networks, and a variety of techniques are needed to manage and mitigate this issue to ensure stable and successful model training.