CODEWITHSUNDEEP
pythonkerasconv-neural-network
Mila Gurova
Mila Gurova

Reputation: 53

CNN with Python and Keras

I'm new to machine learning and Keras. I made an Neural Network with Keras for regression looking like this:

model = Sequential()
model.add(Dense(57, input_dim=44, kernel_initializer='normal', 
activation='relu'))
model.add(Dense(45, activation='relu'))
model.add(Dense(35, activation='relu'))
model.add(Dense(20, activation='relu')) 
model.add(Dense(18, activation='relu'))
model.add(Dense(15, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(5, activation='relu'))
model.add(Dense(5, activation='relu'))
model.add(Dense(1, activation='linear'))

My data after preprocessing has 44 dimensions, so could you please give me an example how could i make an CNN.

Originally it looks like this: https://scontent.fskp1-1.fna.fbcdn.net/v/t1.0-9/40159383_10204721730878434_598395145989128192_n.jpg?_nc_cat=0&_nc_eui2=AeEYA4Nb3gomElC9qt0kF6Ou86P7jidco_LeHxEkmCB0-oVA9YKVe9VAh41SF25YomKTqKdkS96E18-sTCBidxJdbml4OV7FvFuAOWxI4mRafQ&oh=e81f4f56ebdf15e9c6eefbb078b8a982&oe=5BFD4157

Upvotes: 2

Views: 887

Answers (3)

Amir
Amir

Reputation: 16587

Convolution neural network is not the best choice in this case. BTW you can do this thing easily with Conv1d:

model = keras.Sequential()
model.add(keras.layers.Embedding(44, 100))
model.add(keras.layers.Conv1D(50, kernel_size=1, strides=1))
model.add(keras.layers.GlobalAveragePooling1D())
# model.add(keras.layers.Dense(10, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))

Upvotes: 2

Thomas
Thomas

Reputation: 715

You can add 2Dconvnet-layers like this:

model.add(Conv2D(32, (3, 3), input_shape=(3, 150, 150)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

where

model.add(Conv2D(<feature maps>, (<kernel size>), input_shape=(<imput-tensor-shape)))

But be careful, 2Dconfnet-layers are mathematically different than dense-layers, so you can't stack them easily. To stack 2Dconvnet-layers with dense layers, you'll have to flatten them (you'll normally do this at the end to get your "fully-connected layer"):

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))

You'll find a lot of good tutorials on creating conv-nets with keras. This one for example focuses on image recognition. The examples above are taken from this article.

To find out, what a convolutional network does, I'd recommend you this article.

Edit: But I share the opinion, that it might not be useful to use 2DConvnet layers for your example. Your data structure seems kind of "flat" and 2Dconvnets only make sense, when you have some multidimensional tensors as inputs.

Upvotes: 1

Abhijit Balaji
Abhijit Balaji

Reputation: 1940

To answer your question upfront I don't think you can use CNNs for your problem. Generally when people say they are using CNNs they usually mean the 2D convolution. It is operated on 2D spatial data (images). In NLP there exists 1D Convolution which people use to find local patterns in sequentual data. I don't think 1D convolution is relevant in your case. If you are from ML background you can think of regression using feed forward neural networks as polynomial regression. Intuitively you let the network decide which polynomial degree should we use to fit the data properly

Upvotes: 1

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