Keras批归一化和样本权重

Question

I am trying the the training and evaluation example on the tensorflow website . Specifically, this part:

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

x_train = x_train.reshape(60000, 784).astype('float32') / 255
x_test = x_test.reshape(10000, 784).astype('float32') / 255

y_train = y_train.astype('float32')
y_test = y_test.astype('float32')

def get_uncompiled_model():
  inputs = keras.Input(shape=(784,), name='digits')
  x = layers.Dense(64, activation='relu', name='dense_1')(inputs)
  x = layers.BatchNormalization()(x)
  x = layers.Dense(64, activation='relu', name='dense_2')(x)
  outputs = layers.Dense(10, activation='softmax', name='predictions')(x)
  model = keras.Model(inputs=inputs, outputs=outputs)
  return model

def get_compiled_model():
  model = get_uncompiled_model()
  model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),
              loss='sparse_categorical_crossentropy',
              metrics=['sparse_categorical_accuracy'])
  return model

sample_weight = np.ones(shape=(len(y_train),))
sample_weight[y_train == 5] = 2.

# Create a Dataset that includes sample weights
# (3rd element in the return tuple).
train_dataset = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train, sample_weight))

# Shuffle and slice the dataset.
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)

model = get_compiled_model()
model.fit(train_dataset, epochs=3)

It appears that if I add the batch normalization layer (this line: x = layers.BatchNormalization()(x) ) I get the following error:

InvalidArgumentError: The second input must be a scalar, but it has shape [64] [[{{node batch_normalization_2/cond/ReadVariableOp/Switch}}]]

Any ideas?

Answer 1

The same code works for me.

The only lines I changed are :

model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=1e-3) to model.compile(optimizer=keras.optimizers.RMSprop(lr=1e-3) (which is version specific)

Then model.fit(train_dataset, epochs=3) to model.fit(train_dataset, epochs=3, steps_per_epoch=30)
Reason : When using iterators as input to a model, you should specify the steps_per_epoch argument

Answer 2

If you just want to use sample weights, you don't have to use tf.data.Dataset , you can simply run:

model.fit(x=x_train, y=y_train, sample_weight=sample_weight, batch_size=64, epochs=3)

and it works for me (when I change learning_rate to lr as @ASHu2 mentioned).

It gets 97% accuracy after 3 epochs:

...
57408/60000 [===========================>..] - ETA: 0s - loss: 0.1010 - sparse_categorical_accuracy: 0.9709
58816/60000 [============================>.] - ETA: 0s - loss: 0.1011 - sparse_categorical_accuracy: 0.9708
60000/60000 [==============================] - 2s 37us/sample - loss: 0.1007 - sparse_categorical_accuracy: 0.9709

I used TF 1.14.0 on windows.

Answer 3

当我将Tensorflow从版本1.14.1更新到2.0.0-rc1时，此问题已解决。