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[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
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