CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasdeep-learning
Dilip Pokhrel
Dilip Pokhrel

Reputation: 36

Tensorflow model is predicting two values instead of one

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)

Upvotes: 0

Views: 232

Answers (1)

taylon
taylon

Reputation: 27

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)])

Upvotes: 1

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