Keras中RNN模型的input_size是多少

Question

I'm just starting with deep learning, and I've been told that Keras would be the best library for beginners.

Before that, for the sake of learning, I built a simple feed forward network using only numpy so I could get the feel of it.

In this case, the shape of the weight matrix was (len(X[0]), num_neurons) . The number of features and the number of neurons. And it worked.

Now, I'm trying to build a simple RNN using Keras. My data has 7 features and the size of the layer would be 128.

But if I do something like model.add(Dense(128, input_dim=(7, 128))) it says it's wrong.

So I have no idea what this input_dim should be.

My data has 5330 data points and 7 features (shape is (5330, 7)). Can someone tell me what the input_dim should be and why?

Thank you.

Answer 1

The input_dim is just the shape of the input you pass to this layer. So:

• input_dim = 7

There are other options, such as:

• input_shape=(7,) -- This argument uses tuples instead of integers, good when your input has more than one dimension
• batch_input_shape=(batch_size,7) -- This is not usually necessary, but you use it in cases you need a fixed batch size (there are a few layer configurations that demand that)

Now, the size of the output in a Dense layer is the units argument. Which is 128 in your case and should be equal to num_neurons .