在學習時訪問keras/tensorflow的準確性

我想訪問神經網路的準確性（或損失），以便使訓練依賴于它（“課程學習”）。有沒有辦法可以訪問自定義層中的模型精度？

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一種方法是創建自定義回呼并在每個 epoch 之后跟蹤準確性（或損失）。然后，您可以在自定義層中的訓練期間訪問此變數。一個缺點是您必須進行設定run_eagerly=True才能model.compile使此方法起作用：

import tensorflow as tf

result_dic = {"epochs": []}

logging_callback = tf.keras.callbacks.LambdaCallback(
                on_epoch_end=lambda epoch, logs:
                result_dic["epochs"].append({
                    'epoch': epoch   1, 
                    'acc': str(logs['acc'])
                }))

class Linear(tf.keras.layers.Layer):
    def __init__(self, units=32, input_dim=32):
        super(Linear, self).__init__()
        w_init = tf.random_normal_initializer()
        self.w = tf.Variable(
            initial_value=w_init(shape=(input_dim, units), dtype="float32"),
            trainable=True,
        )
        b_init = tf.zeros_initializer()
        self.b = tf.Variable(
            initial_value=b_init(shape=(units,), dtype="float32"), trainable=True
        )

    def call(self, inputs):
        if result_dic['epochs']:
          tf.print(result_dic['epochs'][-1])
        return tf.matmul(inputs, self.w)   self.b

inputs = tf.keras.layers.Input((16,))
linear_layer = Linear(32, 16)
x = linear_layer(inputs)
outputs = tf.keras.layers.Dense(1, 'sigmoid')(x)
model = tf.keras.Model(inputs, outputs)
model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(), metrics=['acc'], run_eagerly=True)
x = tf.random.normal((5,16))
y = tf.random.uniform((5,), maxval=2, dtype=tf.int32)

model.fit(x, y, batch_size=2, epochs=4, callbacks=[logging_callback])

Epoch 1/4
3/3 [==============================] - 0s 15ms/step - loss: 0.8525 - acc: 0.0000e 00
Epoch 2/4
{'acc': '0.0', 'epoch': 1}
1/3 [=========>....................] - ETA: 0s - loss: 0.7647 - acc: 0.5000{'acc': '0.0', 'epoch': 1}
{'acc': '0.0', 'epoch': 1}
3/3 [==============================] - 0s 19ms/step - loss: 0.7834 - acc: 0.2000
Epoch 3/4
{'acc': '0.20000000298023224', 'epoch': 2}
1/3 [=========>....................] - ETA: 0s - loss: 0.7253 - acc: 0.5000{'acc': '0.20000000298023224', 'epoch': 2}
{'acc': '0.20000000298023224', 'epoch': 2}
3/3 [==============================] - 0s 18ms/step - loss: 0.7239 - acc: 0.2000
Epoch 4/4
{'acc': '0.20000000298023224', 'epoch': 3}
1/3 [=========>....................] - ETA: 0s - loss: 0.7091 - acc: 0.5000{'acc': '0.20000000298023224', 'epoch': 3}
{'acc': '0.20000000298023224', 'epoch': 3}
3/3 [==============================] - 0s 19ms/step - loss: 0.6662 - acc: 0.6000
<keras.callbacks.History at 0x7f5319f6a910>

除了字典，您還可以使用一個簡單的串列：

import tensorflow as tf

results = [0.0]

class LossAccCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        global results
        results.append(logs['acc'])

class Linear(tf.keras.layers.Layer):
    def __init__(self, units=32, input_dim=32):
        super(Linear, self).__init__()
        w_init = tf.random_normal_initializer()
        self.w = tf.Variable(
            initial_value=w_init(shape=(input_dim, units), dtype="float32"),
            trainable=True,
        )
        b_init = tf.zeros_initializer()
        self.b = tf.Variable(
            initial_value=b_init(shape=(units,), dtype="float32"), trainable=True
        )

    def call(self, inputs):
        tf.print(results[-1])
        return tf.matmul(inputs, self.w)   self.b

epochs = 4
inputs = tf.keras.layers.Input((16,))
linear_layer = Linear(32, 16)
x = linear_layer(inputs)
outputs = tf.keras.layers.Dense(1, 'sigmoid')(x)
model = tf.keras.Model(inputs, outputs)
model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(), metrics=['acc'], run_eagerly=True)
x = tf.random.normal((5,16))
y = tf.random.uniform((5,), maxval=2, dtype=tf.int32)

model.fit(x, y, batch_size=2, epochs=epochs, callbacks=[LossAccCallback()])

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