CODEWITHSUNDEEP
python-3.xtensorflowlinear-regression
Pe Dro
Pe Dro

Reputation: 3033

Unable to understand format of test data while evaluating training model

I am training a regression model that takes approximates the weights for the equation : Y = R+B+G For this, I provide pre-determined values of R, B and G and Y, as training data.

    R    = np.array([-4, -10,  -2,  8, 5, 22,  3],  dtype=float)
    B    = np.array([4, -10,  0,  0, 15, 5,  1],  dtype=float)
    G    = np.array([0, 10,  5,  8, 1, 2,  38],  dtype=float)

    Y    = np.array([0, -10, 3, 16, 21, 29, 42],  dtype=float)

The training batch consisted of 1x3 array corresponding to Ith value of R, B and G.

    RBG = np.array([R,B,G]).transpose()
    print(RBG)

    [[ -4.   4.   0.]
    [-10. -10.  10.]
    [ -2.   0.   5.]
    [  8.   0.   8.]
    [  5.  15.   1.]
    [ 22.   5.   2.]
    [  3.   1.  38.]]

I used a neural network with 3 inputs, 1 dense layer (hidden layer) with 2 neurons and the output layer (output) with a single neuron.

    hidden = tf.keras.layers.Dense(units=2, input_shape=[3])
    output = tf.keras.layers.Dense(units=1)

Further, I trained the model

    model = tf.keras.Sequential([hidden, output])
    model.compile(loss='mean_squared_error', 
    optimizer=tf.keras.optimizers.Adam(0.1))
    history = model.fit(RBG,Y, epochs=500, verbose=False)
    print("Finished training the model")

The loss vs epoch plot was as normal, decreasing and then flat.

But when I tested the model, using random values of R, B and G as

    print(model.predict([[1],[1],[1]]))

expecting the output to be 1+1+1 = 3, but got the Value Error:

    ValueError: Error when checking input: expected dense_2_input to have shape (3,) but got array with shape (1,)

Any idea where I might be getting wrong?

Surprisingly, the only input it responds to, is the training data itself. i.e, 

    print(model.predict(RBG))

    [[ 2.1606684e-07]
    [-3.0000000e+01]
    [-3.2782555e-07]
    [ 2.4000002e+01]
    [ 4.4999996e+01]
    [ 2.9000000e+01]
    [ 4.2000000e+01]]

Upvotes: 0

Views: 37

Answers (1)

Rishabh Sahrawat
Rishabh Sahrawat

Reputation: 2507

As the error says, the problem is in your shape of the input. You need to transpose [[1],[1],[1]] this input then you will have the shape that is expected by the model.

so npq = np.array([[1],[1],[1]]).transpose() and now feed this to model.predict(npq)

Upvotes: 1

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