Skip to main content

Blog

Learn About Our Meetup

5000+ Members

MEETUPS

LEARN, CONNECT, SHARE

Join our meetup, learn, connect, share, and get to know your Toronto AI community. 

JOB POSTINGS

INDEED POSTINGS

Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.

CONTACT

CONNECT WITH US

Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

[D] How can i elaborate texture and statistic features in CNN?

I have a dataset 2200×34 where 1-33 column are features (texture and statistic) and 34th column is the class (0 or 1). I know my dataset is quite poor, but I splitted in 80% training set and 20% validation test.
I’d like to use CNN for classification using these features, my steps are:

– Splitting in training set and validation test;

– Mean normalisation of features;

– Reshaping training set and validation set in order to have 1760x34x1 and 440x34x1 as dimensions;

– Create my model:

opt = SGD(lr=0.0001) model = Sequential() model.add(Conv1D(16, 3, activation="relu", input_shape =(34,1))) model.add(BatchNormalization()) model.add(MaxPooling1D(2)) model.add(Conv1D(32, 3, activation="relu")) model.add(MaxPooling1D(2)) model.add(Flatten()) model.add(Dense(512, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(1, activation="sigmoid")) model.summary() # compile the model model.compile(loss='binary_crossentropy', optimizer= opt, metrics=['accuracy']) 

Sadly my model has bad performance (acc = 55% more or less and loss = 0.69). Do you have any suggestion to increase my performance? Is there something wrong?

Here the model.summary()

Layer (type) Output Shape Param # ================================================================= conv1d_3 (Conv1D) (None, 32, 16) 64 _________________________________________________________________ batch_normalization_1 (Batch (None, 32, 16) 64 _________________________________________________________________ max_pooling1d_3 (MaxPooling1 (None, 16, 16) 0 _________________________________________________________________ conv1d_4 (Conv1D) (None, 14, 32) 1568 _________________________________________________________________ max_pooling1d_4 (MaxPooling1 (None, 7, 32) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 224) 0 _________________________________________________________________ dense_2 (Dense) (None, 512) 115200 _________________________________________________________________ dropout_1 (Dropout) (None, 512) 0 _________________________________________________________________ dense_3 (Dense) (None, 1) 513 ================================================================= 

submitted by /u/Samatarou
[link] [comments]