我正在嘗試從 TensorFlow 的layers.Layer實體中創建一個自定義層。
我正在嘗試制作一個 IIR 濾波器，因此它使用來自輸入層的值并計算輸出序列，如下所示：
y[i] = a0 * x[i] a1 * x[i - 1] b1 * y[i - 1]
其中x是輸入，y是輸出。我這樣定義類：
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
但我不斷收到錯誤
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)
等等。輸入的形狀是(None, 60)（我設定 60 只是為了測驗目的）并且我假設None在訓練時將被批量大小替換？我怎樣才能訪問輸入的值？輸入的實際形狀是什么？這是正確的方法嗎？
編輯：我正在嘗試在模型中實作這一點，如下所示：
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')
uj5u.com熱心網友回復：
不確定你到底想做什么，但我建議Tensorflow只使用操作。下面是一個例子：
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
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