trying perform an image classification using RL but facing error, Any suggestion?
clc;
%% Load Dataset
imds = imageDatastore(‘D:2023thesiswaveletImagesSubsetImages’, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);
% Ensure the dataset is balanced
DataSetInfo = countEachLabel(imds);
% Split Data into Training and Validation sets
[imdsTrain, imdsValidation] = splitEachLabel(imds, 0.80, ‘randomized’);
%% Load Pretrained Network
net = vgg16;
inputSize = net.Layers(1).InputSize;
% Feature extraction layer
featureLayer = ‘fc7’;
% Augment data to match input size of the network
augimdsTrain = augmentedImageDatastore(inputSize, imdsTrain);
augimdsValidation = augmentedImageDatastore(inputSize, imdsValidation);
% Extract Features using Pretrained CNN
trainFeatures = activations(net, augimdsTrain, featureLayer, ‘OutputAs’, ‘rows’);
validationFeatures = activations(net, augimdsValidation, featureLayer, ‘OutputAs’, ‘rows’);
% Extract labels
trainLabels = grp2idx(imdsTrain.Labels);
validationLabels = grp2idx(imdsValidation.Labels);
%% RL Environment Setup
% Define Observation and Action spaces
numFeatures = size(trainFeatures, 2); % Number of features (e.g., 4096 for ‘fc7’ layer)
ObservationInfo = rlNumericSpec([numFeatures, 1], …
‘LowerLimit’, -inf, ‘UpperLimit’, inf, …
‘Name’, ‘Features’);
ObservationInfo.Description = ‘Feature vector extracted from CNN’;
% Define Action space (each action corresponds to a class label)
ActionInfo = rlFiniteSetSpec(1:max(trainLabels)); % Class labels as actions
ActionInfo.Name = ‘Class Labels’;
% Define Step and Reset functions
stepFunction = @(Action, LoggedSignals) stepFunctionRL(Action, trainFeatures, trainLabels, LoggedSignals);
resetFunction = @() resetFunctionRL(trainFeatures);
% Create RL Environment
env = rlFunctionEnv(ObservationInfo, ActionInfo, stepFunction, resetFunction);
%% Define RL Agent
% Define critic network
statePath = [
featureInputLayer(numFeatures, ‘Normalization’, ‘none’, ‘Name’, ‘state’)
fullyConnectedLayer(128, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu1’)
fullyConnectedLayer(64, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu2’)
fullyConnectedLayer(numel(ActionInfo.Elements), ‘Name’, ‘fcOutput’)]; % Number of actions
criticNet = dlnetwork(layerGraph(statePath));
critic = rlQValueFunction(criticNet, ObservationInfo, ActionInfo, ‘ObservationInputNames’, ‘state’);
% Agent options
agentOpts = rlDQNAgentOptions( …
‘ExperienceBufferLength’, 1e5, …
‘DiscountFactor’, 0.99, …
‘MiniBatchSize’, 64, …
‘TargetSmoothFactor’, 1e-3, …
‘TargetUpdateFrequency’, 4);
agent = rlDQNAgent(critic, agentOpts);
%% Train RL Agent
trainOpts = rlTrainingOptions( …
‘MaxEpisodes’, 500, …
‘MaxStepsPerEpisode’, size(trainFeatures, 1), …
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘StopTrainingCriteria’, ‘EpisodeCount’, …
‘StopTrainingValue’, 500);
trainingStats = train(agent, env);
%% Reset Function
function [InitialObservation, LoggedSignals] = resetFunctionRL(Features)
% Reset function for the RL environment
% This function resets the environment to its initial state at the start
% of each new episode.
% Initialize the LoggedSignals structure (holds environment state)
LoggedSignals = struct();
% Set the initial observation index to the first data point
LoggedSignals.CurrentIndex = 1;
% Fetch the initial observation: the first feature vector
% Ensure that the observation is formatted as a column vector [numFeatures, 1]
InitialObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to column vector
% Check if the initial observation matches the expected size
if size(InitialObservation, 2) ~= 1
error(‘InitialObservation must be a column vector of size [numFeatures, 1]’);
end
% Optionally, initialize any other properties you want in LoggedSignals
% (e.g., additional state variables, environment-specific flags)
LoggedSignals.EpisodeStartTime = datetime(‘now’); % Track the start time for each episode
end
%% Step Function
function [NextObservation, Reward, IsDone, LoggedSignals] = stepFunctionRL(Action, Features, Labels, LoggedSignals)
% Step function for the RL environment
idx = LoggedSignals.CurrentIndex;
% Assign reward based on action correctness
correctLabel = Labels(idx); % Correct label for the current feature
if Action == correctLabel
Reward = 1; % Positive reward for correct classification
else
Reward = -1; % Negative reward for incorrect classification
end
% Update index and check if episode is done
LoggedSignals.CurrentIndex = idx + 1;
IsDone = LoggedSignals.CurrentIndex > size(Features, 1);
% Ensure the next observation is a column vector of size [numFeatures, 1]
if ~IsDone
NextObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to ensure column vector [numFeatures x 1]
else
NextObservation = Features(idx, :)’; % Dummy to avoid dimension mismatch
end
end
AND THIS IS THE ERROR
Error using rl.env.MATLABEnvironment/validateEnvironment (line 58)
Environment ‘ObservationInfo’ does not match observation output from reset function. Check the data type, dimensions, and range.
Error in rl.env.rlFunctionEnv (line 82)
validateEnvironment(this);
Error in rlFunctionEnv (line 45)
env = rl.env.rlFunctionEnv(varargin{:});clc;
%% Load Dataset
imds = imageDatastore(‘D:2023thesiswaveletImagesSubsetImages’, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);
% Ensure the dataset is balanced
DataSetInfo = countEachLabel(imds);
% Split Data into Training and Validation sets
[imdsTrain, imdsValidation] = splitEachLabel(imds, 0.80, ‘randomized’);
%% Load Pretrained Network
net = vgg16;
inputSize = net.Layers(1).InputSize;
% Feature extraction layer
featureLayer = ‘fc7’;
% Augment data to match input size of the network
augimdsTrain = augmentedImageDatastore(inputSize, imdsTrain);
augimdsValidation = augmentedImageDatastore(inputSize, imdsValidation);
% Extract Features using Pretrained CNN
trainFeatures = activations(net, augimdsTrain, featureLayer, ‘OutputAs’, ‘rows’);
validationFeatures = activations(net, augimdsValidation, featureLayer, ‘OutputAs’, ‘rows’);
% Extract labels
trainLabels = grp2idx(imdsTrain.Labels);
validationLabels = grp2idx(imdsValidation.Labels);
%% RL Environment Setup
% Define Observation and Action spaces
numFeatures = size(trainFeatures, 2); % Number of features (e.g., 4096 for ‘fc7’ layer)
ObservationInfo = rlNumericSpec([numFeatures, 1], …
‘LowerLimit’, -inf, ‘UpperLimit’, inf, …
‘Name’, ‘Features’);
ObservationInfo.Description = ‘Feature vector extracted from CNN’;
% Define Action space (each action corresponds to a class label)
ActionInfo = rlFiniteSetSpec(1:max(trainLabels)); % Class labels as actions
ActionInfo.Name = ‘Class Labels’;
% Define Step and Reset functions
stepFunction = @(Action, LoggedSignals) stepFunctionRL(Action, trainFeatures, trainLabels, LoggedSignals);
resetFunction = @() resetFunctionRL(trainFeatures);
% Create RL Environment
env = rlFunctionEnv(ObservationInfo, ActionInfo, stepFunction, resetFunction);
%% Define RL Agent
% Define critic network
statePath = [
featureInputLayer(numFeatures, ‘Normalization’, ‘none’, ‘Name’, ‘state’)
fullyConnectedLayer(128, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu1’)
fullyConnectedLayer(64, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu2’)
fullyConnectedLayer(numel(ActionInfo.Elements), ‘Name’, ‘fcOutput’)]; % Number of actions
criticNet = dlnetwork(layerGraph(statePath));
critic = rlQValueFunction(criticNet, ObservationInfo, ActionInfo, ‘ObservationInputNames’, ‘state’);
% Agent options
agentOpts = rlDQNAgentOptions( …
‘ExperienceBufferLength’, 1e5, …
‘DiscountFactor’, 0.99, …
‘MiniBatchSize’, 64, …
‘TargetSmoothFactor’, 1e-3, …
‘TargetUpdateFrequency’, 4);
agent = rlDQNAgent(critic, agentOpts);
%% Train RL Agent
trainOpts = rlTrainingOptions( …
‘MaxEpisodes’, 500, …
‘MaxStepsPerEpisode’, size(trainFeatures, 1), …
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘StopTrainingCriteria’, ‘EpisodeCount’, …
‘StopTrainingValue’, 500);
trainingStats = train(agent, env);
%% Reset Function
function [InitialObservation, LoggedSignals] = resetFunctionRL(Features)
% Reset function for the RL environment
% This function resets the environment to its initial state at the start
% of each new episode.
% Initialize the LoggedSignals structure (holds environment state)
LoggedSignals = struct();
% Set the initial observation index to the first data point
LoggedSignals.CurrentIndex = 1;
% Fetch the initial observation: the first feature vector
% Ensure that the observation is formatted as a column vector [numFeatures, 1]
InitialObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to column vector
% Check if the initial observation matches the expected size
if size(InitialObservation, 2) ~= 1
error(‘InitialObservation must be a column vector of size [numFeatures, 1]’);
end
% Optionally, initialize any other properties you want in LoggedSignals
% (e.g., additional state variables, environment-specific flags)
LoggedSignals.EpisodeStartTime = datetime(‘now’); % Track the start time for each episode
end
%% Step Function
function [NextObservation, Reward, IsDone, LoggedSignals] = stepFunctionRL(Action, Features, Labels, LoggedSignals)
% Step function for the RL environment
idx = LoggedSignals.CurrentIndex;
% Assign reward based on action correctness
correctLabel = Labels(idx); % Correct label for the current feature
if Action == correctLabel
Reward = 1; % Positive reward for correct classification
else
Reward = -1; % Negative reward for incorrect classification
end
% Update index and check if episode is done
LoggedSignals.CurrentIndex = idx + 1;
IsDone = LoggedSignals.CurrentIndex > size(Features, 1);
% Ensure the next observation is a column vector of size [numFeatures, 1]
if ~IsDone
NextObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to ensure column vector [numFeatures x 1]
else
NextObservation = Features(idx, :)’; % Dummy to avoid dimension mismatch
end
end
AND THIS IS THE ERROR
Error using rl.env.MATLABEnvironment/validateEnvironment (line 58)
Environment ‘ObservationInfo’ does not match observation output from reset function. Check the data type, dimensions, and range.
Error in rl.env.rlFunctionEnv (line 82)
validateEnvironment(this);
Error in rlFunctionEnv (line 45)
env = rl.env.rlFunctionEnv(varargin{:}); clc;
%% Load Dataset
imds = imageDatastore(‘D:2023thesiswaveletImagesSubsetImages’, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);
% Ensure the dataset is balanced
DataSetInfo = countEachLabel(imds);
% Split Data into Training and Validation sets
[imdsTrain, imdsValidation] = splitEachLabel(imds, 0.80, ‘randomized’);
%% Load Pretrained Network
net = vgg16;
inputSize = net.Layers(1).InputSize;
% Feature extraction layer
featureLayer = ‘fc7’;
% Augment data to match input size of the network
augimdsTrain = augmentedImageDatastore(inputSize, imdsTrain);
augimdsValidation = augmentedImageDatastore(inputSize, imdsValidation);
% Extract Features using Pretrained CNN
trainFeatures = activations(net, augimdsTrain, featureLayer, ‘OutputAs’, ‘rows’);
validationFeatures = activations(net, augimdsValidation, featureLayer, ‘OutputAs’, ‘rows’);
% Extract labels
trainLabels = grp2idx(imdsTrain.Labels);
validationLabels = grp2idx(imdsValidation.Labels);
%% RL Environment Setup
% Define Observation and Action spaces
numFeatures = size(trainFeatures, 2); % Number of features (e.g., 4096 for ‘fc7’ layer)
ObservationInfo = rlNumericSpec([numFeatures, 1], …
‘LowerLimit’, -inf, ‘UpperLimit’, inf, …
‘Name’, ‘Features’);
ObservationInfo.Description = ‘Feature vector extracted from CNN’;
% Define Action space (each action corresponds to a class label)
ActionInfo = rlFiniteSetSpec(1:max(trainLabels)); % Class labels as actions
ActionInfo.Name = ‘Class Labels’;
% Define Step and Reset functions
stepFunction = @(Action, LoggedSignals) stepFunctionRL(Action, trainFeatures, trainLabels, LoggedSignals);
resetFunction = @() resetFunctionRL(trainFeatures);
% Create RL Environment
env = rlFunctionEnv(ObservationInfo, ActionInfo, stepFunction, resetFunction);
%% Define RL Agent
% Define critic network
statePath = [
featureInputLayer(numFeatures, ‘Normalization’, ‘none’, ‘Name’, ‘state’)
fullyConnectedLayer(128, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu1’)
fullyConnectedLayer(64, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu2’)
fullyConnectedLayer(numel(ActionInfo.Elements), ‘Name’, ‘fcOutput’)]; % Number of actions
criticNet = dlnetwork(layerGraph(statePath));
critic = rlQValueFunction(criticNet, ObservationInfo, ActionInfo, ‘ObservationInputNames’, ‘state’);
% Agent options
agentOpts = rlDQNAgentOptions( …
‘ExperienceBufferLength’, 1e5, …
‘DiscountFactor’, 0.99, …
‘MiniBatchSize’, 64, …
‘TargetSmoothFactor’, 1e-3, …
‘TargetUpdateFrequency’, 4);
agent = rlDQNAgent(critic, agentOpts);
%% Train RL Agent
trainOpts = rlTrainingOptions( …
‘MaxEpisodes’, 500, …
‘MaxStepsPerEpisode’, size(trainFeatures, 1), …
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘StopTrainingCriteria’, ‘EpisodeCount’, …
‘StopTrainingValue’, 500);
trainingStats = train(agent, env);
%% Reset Function
function [InitialObservation, LoggedSignals] = resetFunctionRL(Features)
% Reset function for the RL environment
% This function resets the environment to its initial state at the start
% of each new episode.
% Initialize the LoggedSignals structure (holds environment state)
LoggedSignals = struct();
% Set the initial observation index to the first data point
LoggedSignals.CurrentIndex = 1;
% Fetch the initial observation: the first feature vector
% Ensure that the observation is formatted as a column vector [numFeatures, 1]
InitialObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to column vector
% Check if the initial observation matches the expected size
if size(InitialObservation, 2) ~= 1
error(‘InitialObservation must be a column vector of size [numFeatures, 1]’);
end
% Optionally, initialize any other properties you want in LoggedSignals
% (e.g., additional state variables, environment-specific flags)
LoggedSignals.EpisodeStartTime = datetime(‘now’); % Track the start time for each episode
end
%% Step Function
function [NextObservation, Reward, IsDone, LoggedSignals] = stepFunctionRL(Action, Features, Labels, LoggedSignals)
% Step function for the RL environment
idx = LoggedSignals.CurrentIndex;
% Assign reward based on action correctness
correctLabel = Labels(idx); % Correct label for the current feature
if Action == correctLabel
Reward = 1; % Positive reward for correct classification
else
Reward = -1; % Negative reward for incorrect classification
end
% Update index and check if episode is done
LoggedSignals.CurrentIndex = idx + 1;
IsDone = LoggedSignals.CurrentIndex > size(Features, 1);
% Ensure the next observation is a column vector of size [numFeatures, 1]
if ~IsDone
NextObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to ensure column vector [numFeatures x 1]
else
NextObservation = Features(idx, :)’; % Dummy to avoid dimension mismatch
end
end
AND THIS IS THE ERROR
Error using rl.env.MATLABEnvironment/validateEnvironment (line 58)
Environment ‘ObservationInfo’ does not match observation output from reset function. Check the data type, dimensions, and range.
Error in rl.env.rlFunctionEnv (line 82)
validateEnvironment(this);
Error in rlFunctionEnv (line 45)
env = rl.env.rlFunctionEnv(varargin{:}); rl, image-classification MATLAB Answers — New Questions
