How to increase model's efficiency using CNN
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I'm using normalized data ranging [-1,1]. I have to used 1 dimensional convolution. But my model is only showing 74 percent training and test accuracy. I want to increase this accuracy. I have increases the amount of input data and and tried to add 2 convolution layer but still there is no effect on accuracy. I have to classify data into two classes. Can someone please guide me what should I do to increase accuracy. Or suggest some CNN network that is using data [-1,1]. Moreover should I add more fully connected layer or should I increase convolutional layers which will be more effective? input = tf.reshape(x, [-1, FLAGS.image_width, FLAGS.input_channel]) filter = weight_variable([FLAGS.filter_width, FLAGS.input_channel, FLAGS.filter_channel]) conv_out = tf.nn.tanh(conv1d(input, filter)) poo