How to correctly arrange data for keras.model.fit()-CodePudding

I'm working in a neural network using keras (from tensorflow) for a college project. I'm pretty new to the library, so I don't really know how should I feed the data into the model in order for the training to work. I've been searching the internet for hours and I can't find a proper tutorial / documentation on how to do it.

Here's the model I'm using, one of the simplest possible ones:

model = keras.Sequential([
    keras.layers.Dense(20, input_dim=1,activation = activations.relu),
    keras.layers.Dense(10, activation=  activations.relu),
    keras.layers.Dense(8, activation= activations.sigmoid)
])

model.compile(optimizer = "adam", loss = "sparse_categorical_crossentropy",
              metrics = ["accuracy"])

The input to the network is a list of 20 floats, and the output a list of 8 floats ranged from 0 to 1 (confidence level), so I think this model is OK, please let me know if I'm wrong.

Here's a diagram of the model i'm trying to build and train:

Let's say I have:

10 input examples (10 lists of 20 floats) for the expected output [1,0,0,0,0,0,0,0]
10 input examples for the expect output [0,1,0,0,0,0,0,0,0,0]
...
10 input examples for the expected output [0,0,0,0,0,0,0,1]

How should I prepare this data in order to use it with

model.fit(training_inputs,expected_outputs,epochs = NUM_EPOCHS) ?

What should training_inputs exactly be? and expected_outputs?

Any help will be appreciated. Thank you for your time!

CodePudding user response：

First of all, you have two issues in your model. According to your description, your input data is 20-dimensional, so in the first layer you should have input_dim=20. Then, you have a cross-entropy loss, so I'm assuming that you are training a 8-class classifier. If that's the case, then instead of keras.layers.Dense(8, activation= activations.sigmoid) you should use

keras.layers.Dense(8, activation=None),
keras.layers.Softmax()

as that ensures that you get a distribution over classes for each input data point.

Now regarding your input data question, training_inputs should a tensor (or numpy array, which will be readily converted) with shape (n_points, 20) in your case. Accordingly, expected_outputs should have shape (n_points, 8). So, just concatenate/stack your input data along the first dimension (axis=0), such that each row corresponds to your 20-dimensional data points. You do the same for expected_outputs, maybe something like,

expected_outputs = np.r_[
    np.tile([[1,0,0,0,0,0,0,0]], (10, 1)),
    np.tile([[0,1,0,0,0,0,0,0]], (10, 1)),
    ...
    np.tile([[0,0,0,0,0,0,0,1]], (10, 1)),
]

Remember to set batch_size and shuffle!