tensorflow - 如何在 Keras 中调用方法作为自定义回调？

Question

我需要在每 5K 次迭代后运行以下方法。

def evaluation_matrix(path_true,path_pred):
    print(path_true,"\n",path_pred)

    true_list_new, pred_list_new = read_from_folder(path_true = path_true , path_pred = path_pred)

    try:
        scikit_metrix(true_list_new = true_list_new,pred_list_new = pred_list_new)
    except:
        print("An exception occurred")

我希望将它用作 model.fit_generator 函数中的回调。如何做到这一点？那就是传参+5K区间？

history = model.fit_generator(generator = myGene, steps_per_epoch=steps_per_epoch, epochs=epoch, verbose = 1, callbacks=[],shuffle=True)

标签： tensorflowmachine-learningkeras

Answer 1

自定义回调是在训练、评估或推理期间自定义 Keras 模型行为的强大工具。

下面是一个示例，我们在每个 epoch 之后计算梯度。同样，您可以使用许多内置方法进行更多自定义。您可以在此处找到更多相关信息 - https://www.tensorflow.org/guide/keras/custom_callback

注意：我使用的是 tensorflow 1.15.0

# (1) Importing dependency
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np

np.random.seed(1000)

# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)

# (3) Create a sequential model
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=96, input_shape=(224,224,3), kernel_size=(11,11), strides=(4,4), padding='valid'))
model.add(Activation('relu'))
# Pooling 
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation before passing it to the next layer
model.add(BatchNormalization())

# 2nd Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(11,11), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

# 3rd Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Batch Normalisation
model.add(BatchNormalization())

# 4th Convolutional Layer
model.add(Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Batch Normalisation
model.add(BatchNormalization())

# 5th Convolutional Layer
model.add(Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Batch Normalisation
model.add(BatchNormalization())

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(4096, input_shape=(224*224*3,)))
model.add(Activation('relu'))
# Add Dropout to prevent overfitting
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# 2nd Dense Layer
model.add(Dense(4096))
model.add(Activation('relu'))
# Add Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# 3rd Dense Layer
model.add(Dense(1000))
model.add(Activation('relu'))
# Add Dropout
model.add(Dropout(0.4))
# Batch Normalisation
model.add(BatchNormalization())

# Output Layer
model.add(Dense(17))
model.add(Activation('softmax'))

model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
epoch_gradient = []

# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
  def get_gradient_func(model):
      grads = K.gradients(model.total_loss, model.trainable_weights)
      inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
      func = K.function(inputs, grads)
      return func
  def on_epoch_end(self, epoch, logs=None):
      get_gradient = get_gradient_func(model)
      grads = get_gradient([x, y, np.ones(len(y))])
      epoch_gradient.append(grads)

model.fit(x, y, batch_size=64, epochs= 4, verbose=1, validation_split=0.2, shuffle=True, callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)
print("Gradient Array has the shape:",gradient.shape)