I use AdaBoostClassifier from sklearn.ensemble. I trained my model using 1000 estimators:
model = AdaBoostClassifier(
base_estimator = DecisionTreeClassifier(max_depth = 6),
n_estimators = 1000,
learning_rate = 0.2
)
model.fit(X_train, y_train)
then using generator model.staged_predict_proba(X_test) I get know that the best accuracy for X_test data is for 814 estimators.
Now I don't want to use generator model.staged_predict_proba(X_test) to make prediction for new test data X_test_2 because it is a lot of time to calculate predictions for each number of estimators (the dataset is really big). I just want to calculate predictions for model based on 814 estimators. I didn't find a way to do it. Is it possible for AdaBoostClassifier? I think it should be.
I have a network model that is trained using batch training. Once it is trained, I want to predict the output for a single example.
Here is my model code:
model = Sequential()
model.add(Dense(32, batch_input_shape=(5, 1, 1)))
model.add(LSTM(16, stateful=True))
model.add(Dense(1, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
I have a sequence of single inputs to single outputs. I'm doing some test code to map characters to next characters (A->B, B->C, etc).
I create an input data of shape (15,1,1) and an output data of shape (15, 1) and call the function:
model.fit(x, y, nb_epoch=epochs, batch_size=5, shuffle=False, verbose=0)
The model trains, and now I want to take a single character and predict the next character (input A, it predicts B). I create an input of shape (1, 1, 1) and call:
pred = model.predict(x, batch_size=1, verbose=0)
This gives:
ValueError: Shape mismatch: x has 5 rows but z has 1 rows
I saw one solution was to add "dummy data" to your predict values, so the input shape for the prediction would be (5,1,1) with data [x 0 0 0 0] and you would just take the first element of the output as your value. However, this seems inefficient when dealing with larger batches.
I also tried to remove the batch size from the model creation, but I got the following message:
ValueError: If a RNN is stateful, a complete input_shape must be provided (including batch size).
Is there another way? Thanks for the help.
Currently (Keras v2.0.8) it takes a bit more effort to get predictions on single rows after training in batch.
Basically, the batch_size is fixed at training time, and has to be the same at prediction time.
The workaround right now is to take the weights from the trained model, and use those as the weights in a new model you've just created, which has a batch_size of 1.
The quick code for that is
model = create_model(batch_size=64)
mode.fit(X, y)
weights = model.get_weights()
single_item_model = create_model(batch_size=1)
single_item_model.set_weights(weights)
single_item_model.compile(compile_params)
Here's a blog post that goes into more depth:
https://machinelearningmastery.com/use-different-batch-sizes-training-predicting-python-keras/
I've used this approach in the past to have multiple models at prediction time- one that makes predictions on big batches, one that makes predictions on small batches, and one that makes predictions on single items. Since batch predictions are much more efficient, this gives us the flexibility to take in any number of prediction rows (not just a number that is evenly divisible by batch_size), while still getting predictions pretty rapidly.
#ClimbsRocks showed a nice workaround. I cannot provide a "correct" answer in sense of "this is how Keras intends it to be done", but I can share another workaround which might help somebody depending on the use-case.
In this workaround I use predict_on_batch(). This method allows to pass a single sample out of a batch without throwing an error. Unfortunately, it returns a vector in the shape the target has according to the training-settings. However, each sample in the target yields then the prediction for your single sample.
You can access it like this:
to_predict = #Some single sample that would be part of a batch (has to have the right shape)#
model.predict_on_batch(to_predict)[0].flatten() #Flatten is optional
The result of the prediction is exactly the same as if you would pass an entire batch to predict().
Here some cod-example.
The code is from my question which also deals with this issue (but in a sligthly different manner).
sequence_size = 5
number_of_features = 1
input = (sequence_size, number_of_features)
batch_size = 2
model = Sequential()
#Of course you can replace the Gated Recurrent Unit with a LSTM-layer
model.add(GRU(100, return_sequences=True, activation='relu', input_shape=input, batch_size=2, name="GRU"))
model.add(GRU(1, return_sequences=True, activation='relu', input_shape=input, batch_size=batch_size, name="GRU2"))
model.compile(optimizer='adam', loss='mse')
model.summary()
#Summary-output:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
GRU (GRU) (2, 5, 100) 30600
_________________________________________________________________
GRU2 (GRU) (2, 5, 1) 306
=================================================================
Total params: 30,906
Trainable params: 30,906
Non-trainable params: 0
def generator(data, batch_size, sequence_size, num_features):
"""Simple generator"""
while True:
for i in range(len(data) - (sequence_size * batch_size + sequence_size) + 1):
start = i
end = i + (sequence_size * batch_size)
yield data[start : end].reshape(batch_size, sequence_size, num_features), \
data[end - ((sequence_size * batch_size) - sequence_size) : end + sequence_size].reshape(batch_size, sequence_size, num_features)
#Task: Predict the continuation of a linear range
data = np.arange(100)
hist = model.fit_generator(
generator=generator(data, batch_size, sequence_size, num_features),
steps_per_epoch=total_batches,
epochs=200,
shuffle=False
)
to_predict = np.asarray([[np.asarray([x]) for x in range(95,100,1)]]) #Only single element of a batch
correct = np.asarray([100,101,102,103,104])
print( model.predict_on_batch(to_predict)[0].flatten() )
#Output:
[ 99.92908 100.95854 102.32129 103.28584 104.20213 ]
I'm new in machine learning (and to stackoverflow as well) and i want to make some classification tasks. I performed two group classifications on my data set (field of speech acoustics) with LIBSVM and Matlab's Pattern Recignition Tool from the Neural network toolbox to create a simple network with one hidden layer. In the hope of higher classification results i want to try Deep Neural Networks, and i found this code: http://www.mathworks.com/matlabcentral/fileexchange/42853-deep-neural-network
I have some difficulty understanding it.
My data is constructed of 127 samples of 19 parameters, so my input number is 19. I want to classify them in two groups: 0 and 1, so my output number is 1. The values in my data set are normalized between 0 and 1.
My code is the following:
clear all
clc
addpath('..');
load('data.mat')
inputdata = inputs;
outputdata = outputs;
datanum = 127;
outputnum = 1;
hiddennum = 3;
inputnum = 19;
% rbm = randRBM(inputnum, outputnum);
% rbm = pretrainRBM( rbm, inputdata );
dbn = randDBN([inputnum, hiddennum, outputnum]);
dbn = pretrainDBN( dbn, inputdata );
dbn = SetLinearMapping( dbn, inputdata, outputdata );
dbn = trainDBN( dbn, inputdata, outputdata );
estimate = v2h( dbn, inputdata )
[rmse AveErrNum] = CalcRmse(dbn, inputdata, outputdata)
The code runs. The rmse is 0.4183, the AveErrNum is 0.1969. What i need is the classification accuracy between my targets (stored in outputdata) and the networks predictions (Accuracy = data classified correctly / all data).
Where do i find the networks predictions after binarization?
Do I use the right type of network for my classification?
Don't I need to divide my data into Training, Validation and Testing samples (like in the case of a simple neural network with one hidden layer)?
Thanks in advance for any help!
I'm trying to show the difference between results of an ANN trained with and without validation...
assume that I'm trying to train a ANN how a sin function should work:
this is gonna be my training data:
x = -1:0.05:1;
t = sin(2*pi*x)+0.01*randn(size(x));
and for validation data I gonna use this:
val.X = -0.975:.05:0.975;
val.T = sin(2*pi*val.X)+0.01*randn(size(val.X));
then I configure my net as follows:
net = feedforwardnet(10,'trainlm');
net.trainParam.show = 50;
net.trainParam.epochs = 300;
then I train net without validation :
[net1, tr1] = train(net,x,t);
and for training with validation I use this code:
[net2,tr2]=train(net,x,t,[],[],val);
but it doesn't work!?
EDIT:
the error says: "Error weights EW is not a matrix or cell array."
I wonder if you could tell me how to validate training ANN by custom data!?
I m trying to simulate out of sample prediction of a binary classifier using libsvm in matlab. My target variable (ie my label) is binary (-1 +1). Therefore, in my test set there are series for which i don t know the label. I created a new label for these observations (this label is 747). I found that in my predicted_label_test vector (see code below), this 747 label is included. So it means the prediction I get is influenced by the labels of the data included the test set, which is what I m supposed to predict? - The mistake may be in the way I use Libsvm read and write functions but i can t find it - many thanks!!
%%%%%%%%%% GET DATA FROM THE CSV FILE AND CONVERT THEM TO LIBSVM
addpath('C:\libsvm1\matlab'); %indicate position of the CSV file
ALLDATA = csvread('DATACSV.csv'); % read a csv file
labels = ALLDATA(:, 1); % labels are included in the first column of data
labels_sparse = sparse (labels); %? needed
features = ALLDATA(:, 4:end); % features start at 4th column
features_sparse = sparse(features); % features must be in a sparse matrix
libsvmwrite('TTT.train', labels_sparse, features_sparse); % write the file to libsvm format
[label_vector, predictors_matrix] = libsvmread('C:\libsvm1\matlab\TTT.train'); % read the file that was recorded in Libsvm format
%%%%% DEFINE VECTOR AND MATRIX SIZE
label_vector_train = label_vector (1:143,:);
predictors_matrix_train = predictors_matrix (1:143,:);
label_vector_test = label_vector (144:193,:);
predictors_matrix_test = predictors_matrix (144:193,:);
%PREDICTION
param = ['-q -c 2 -g 3'];
bestModel = svmtrain(label_vector_test, predictors_matrix_test, param);
[predicted_label_test, accuracy, prob_values] = svmpredict(label_vector_test, predictors_matrix_test, bestModel);
You are training a svm model with test data, when you should train it with training data:
bestModel = svmtrain(label_vector_test, predictors_matrix_test, param);
should be:
bestModel = svmtrain(label_vector_train, predictors_matrix_train, param);