TensorFlow layer subclass input shape-CodePudding

I am trying to make a custom layer from TensorFlow's layers.Layer instance.

I am trying to make a IIR filter, so that's using values from the input layer and computing an output sequence, something like this:

y[i] = a0 * x[i]   a1 * x[i - 1]   b1 * y[i - 1]

where x is the input and y is the output. I define the class this way:

class IIR(keras.layers.Layer):
    def __init__(self, input_dim):
        super(IIR, self).__init__()
        self.input_dim = 60
        self.b0 = tf.Variable(tf.constant([uniform(-1, 1)]))
        self.b1 = tf.Variable(tf.constant([uniform(-1, 1)]))
        self.b2 = tf.Variable(tf.constant([uniform(-1, 1)]))
        self.a1 = tf.Variable(tf.constant([uniform(-1, 1)]))
        self.a2 = tf.Variable(tf.constant([uniform(-1, 1)]))
        
    def call(self, inputs):
      order = 3
      init_dim = [0,1,2]
      output_sequence = tf.constant(np.zeros((self.input_dim)),dtype=tf.float32)
      outt = np.zeros(self.input_dim)
      outt[0] = inputs[0]
      outt[1] = inputs[1]
      outt[2] = inputs[2]
      for i in range(2,self.input_dim):
        outt[i] = self.b0*inputs[i]   self.b1*inputs[i-1]   self.b2*inputs[i-2] - self.a1*outt[i-1] - self.a2*outt[i-2]
      output_sequence = tf.constant(outt)
      return output_sequence

but I keep getting the error

ValueError: Exception encountered when calling layer "iir_13" (type IIR).

in user code:

    File "<ipython-input-37-0717fc982e73>", line 17, in call  *
        outt[0] = inputs[:][0]

    ValueError: setting an array element with a sequence.


Call arguments received:
  • inputs=tf.Tensor(shape=(None, 60), dtype=float32)

and so on. The shape of the input is (None, 60) (I'm setting 60 just for testing purposes) and I am assuming None will be replaced by batch size when training? How can I have access to the values of the input? What's the actual shape of the input? Is this the right approach?

EDIT: I am trying to implement this in a model, something like this:

model = keras.Sequential()
model.add(keras.layers.Input(shape=60))
model.add(IIR(input_dim=60))
model.add(keras.layers.Dense(8, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy')

CodePudding user response：

Not sure what exactly you want to do, but I would recommend using Tensorflow operations only. Here is an example:

import tensorflow as tf

class IIR(tf.keras.layers.Layer):

    def __init__(self, input_dim):
        super(IIR, self).__init__()
        self.input_dim = input_dim
        self.b0 = tf.Variable(tf.random.uniform((1,), minval=-1, maxval=1))
        self.b1 = tf.Variable(tf.random.uniform((1,), minval=-1, maxval=1))
        self.b2 = tf.Variable(tf.random.uniform((1,), minval=-1, maxval=1))
        self.a1 = tf.Variable(tf.random.uniform((1,), minval=-1, maxval=1))
        self.a2 = tf.Variable(tf.random.uniform((1,), minval=-1, maxval=1))
        

    def call(self, inputs):
      batch_size = tf.shape(inputs)[0]
      output_sequence = tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True, clear_after_read=False) 
      output_sequence = output_sequence.write(0, inputs[:, 0])
      output_sequence = output_sequence.write(1, inputs[:, 1])
      output_sequence = output_sequence.write(2, inputs[:, 2])

      for i in range(2, self.input_dim):
        output_sequence = output_sequence.write(i, self.b0*inputs[:, i]   self.b1*inputs[:, i-1] 
                                                   self.b2*inputs[:, i-2] - self.a1*output_sequence.read(i-1)
                                                 - self.a2*output_sequence.read(i-2))
      result = output_sequence.stack()
      return tf.reshape(result, tf.shape(inputs))

iir = IIR(input_dim=60)
tf.print(iir(tf.random.normal((2, 60))).shape)

iir = IIR(input_dim=60)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=60))
model.add(IIR(input_dim=60))
model.add(tf.keras.layers.Dense(8, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
print(model.summary())

TensorShape([2, 60])
Model: "sequential_21"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 iir_80 (IIR)                (None, 60)                5         
                                                                 
 dense_20 (Dense)            (None, 8)                 488       
                                                                 
 dense_21 (Dense)            (None, 1)                 9         
                                                                 
=================================================================
Total params: 502
Trainable params: 502
Non-trainable params: 0
_________________________________________________________________
None