Build a basic model for arrays of arrays data samples

TensorFlow General Discussion
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1

Hi all, I’m trying to build a “simple” model to predict basic arm movements. Every movement as described as an array of a variable length, which contains sub-arrays of fixed length = 3 (i.e. something like this: [[1,2,3],[4,5,6], … , [100,102,103]]).

I’ve tried to build a model this way, but I guess something is wrong because I’m getting error when i try to make the prediction (the training seems ok):

import numpy as np
import tensorflow as tf

# Create the model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(3, activation='relu', input_shape=(None, 9)),  # Adjusted input shape
    tf.keras.layers.Dense(2, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
data = np.array([[[2, 3, 5], [6, 8, 9], [10, 11, 12]], [[2, 1, 3], [3, 5, 6], [4, 5, 2]]])
labels = np.array([0, 1])  # One label for each group of arrays

# Reshape the data to combine all groups into one sample
data = np.reshape(data, (2, -1))

model.fit(data, labels, epochs=10)



to_predict = np.array([[2,14,3],[3,56,6]])

predictions = model.predict(to_predict)
print(predictions)
3

Hi @Quiquoqua48, You should add padding to to_predict array so that it should match the input_shape of the model.

to_predict = np.array([[2,14,3],[3,56,6]])
x=np.pad(to_predict,((0, 0), (0, 6)), 'constant', constant_values=0)
predictions = model.predict(x)
print(predictions)

Now you can make predictions using that. Please refer to this gist for working code example.

5

Thank you for your answer! It solve the prediction problem, but I’ve another problem now, I think that the model as now doesn’t make what I guessed… I’ll try to explain, maybe you can help me:

the result, i.e. [[0.5 0.5 ], [0.49962947 0.50037056]], if I correctly understand, considers [2,14,3] and [3,56,6] as different input to calculate the prediction on, while I would like to have a prediction on [[2,14,3],[3,56,6]] as single data (also [[2,14,3],[3,56,6],[3,4,6]] should be ok… my problem is not the length, but the fact that I need to have a percent result for one list of arrays considered as one single sample). Is there a way to perform that?

6

Hi @Quiquoqua48, As you have defined the model input_shape as (None , 9), while making predictions the model will accept the same shape. Even when you try to make predictions using [[2,14,3],[3,56,6],[3,4,6]] you will get an error because the 2nd dimension has only 3 elements but the model accepts 9 elements. To make predictions make sure that the 2nd dimensions should have 9 elements. Thank You.