Tensorflow model is predicting two values instead of one

Question

I am making a simple model which predicts sum of two values given in input. I have also flattened the array into shape (2,) but still the model is predicting two values. In predict function I am getting the shape of (4,2) instead of (4,1). why is this happening?

xs = np.array([[[[1.0],[2.0]]],[[[2.0],[6.0]]],[[[3.0], [4.0]]],[[[5.0], [6.0]]]],dtype=float)
    
ys = np.array([[3.0],[8.0],[7.0],[11.0]], dtype=float)
    
tf.random.set_seed(12)
    
model=tf.keras.Sequential([tf.keras.layers.Dense(1,tf.keras.layers.Flatten(input_shape=(2,)))])
    
model.compile(loss="mean_squared_error",optimizer=tf.keras.optimizers.Adam(learning_rate=0.1))

model.fit(xs,ys, epochs=500, verbose=False)
    
    
model.predict([[[[1.0],[2.0]]],[[[2.0],[6.0]]],[[[3.0],[4.0]]],[[[5.0],[6.0]]]])

The predictions are coming as follows:

array([[ 3.9273381,  5.193098 ],
       [ 5.193098 , 10.256137 ],
       [ 6.4588575,  7.724617 ],
       [ 8.990376 , 10.256137 ]], dtype=float32)

taylon · Accepted Answer · 2022-07-16 03:42:13Z

1

You should add the flatten layer before your dense layer, so something like this:

model=tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(2,)),
                           tf.keras.layers.Dense(1)])

answered Jul 16, 2022 at 3:42

taylon

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2 Comments

Dilip Pokhrel Over a year ago

Thank you for the solution. In my previous code, I have put flatten function into dense layer. was my code actually flattening the array after calculating the weighted sum in that layer?

taylon Over a year ago

I'm actually not sure, lol. Some weird things happen when you pass layers as arguments into other layers I guess

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