我正在使用Keras 2.1.3和Tensorflow后端处理多类分类问题。我有两个numpy数组，x和y，并且我正在像这样使用tf.data.Dataset：
dataset = tf.data.Dataset.from_tensor_slices(({"sequence": x}, y))
dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(self.batch_size))
dataset = dataset.repeat()
xt, yt = dataset.make_one_shot_iterator().get_next()
然后，我制作我的Keras模型（为简洁起见，省略），进行编译并拟合：
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'],
)
model.fit(xt, yt, steps_per_epoch=100, epochs=10)
这非常好用。但是，当我添加回调时，就会遇到问题。具体来说，如果我这样做：
callbacks = [
tf.keras.callbacks.ModelCheckpoint("model_{epoch:04d}_{val_acc:.4f}.h5",
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max'),
tf.keras.callbacks.TensorBoard(os.path.join('.', 'logs')),
tf.keras.callbacks.EarlyStopping(monitor='val_acc', patience=5, min_delta=0, mode='max')
]
model.fit(xt, yt, steps_per_epoch=10, epochs=100, callbacks=callbacks)
我得到：
KeyError: 'val_acc'
另外，如果我在validation_split=0.1通话中加入model.fit(...)，也会被告知：
ValueError: If your data is in the form of symbolic tensors, you cannot use validation_split。`
在tf.data.Dataset（张量）中使用回调和验证拆分的正常方法是什么？
谢谢！
答案 0 :(得分：1)
使用tensorflow keras API，您可以提供Dataset用于培训，并提供另一个用于验证。
首先进口
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Dense
import numpy as np
定义将numpy数组拆分为training / val的函数
def split(x, y, val_size=50):
idx = np.random.choice(x.shape[0], size=val_size, replace=False)
not_idx = list(set(range(x.shape[0])).difference(set(idx)))
x_val = x[idx]
y_val = y[idx]
x_train = x[not_idx]
y_train = y[not_idx]
return x_train, y_train, x_val, y_val
定义numpy数组和train / val张量流Datasets
x = np.random.randn(150, 9)
y = np.random.randint(0, 10, 150)
x_train, y_train, x_val, y_val = split(x, y)
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, tf.one_hot(y_train, depth=10)))
train_dataset = train_dataset.batch(32).repeat()
val_dataset = tf.data.Dataset.from_tensor_slices((x_val, tf.one_hot(y_val, depth=10)))
val_dataset = val_dataset.batch(32).repeat()
制作模型（注意，我们正在使用tensorflow keras API）
model = keras.models.Sequential([Dense(64, input_shape=(9,), activation='relu'),
Dense(64, activation='relu'),
Dense(10, activation='softmax')
])
model.compile(optimizer='Adam', loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
model.fit(train_dataset,
epochs=10,
steps_per_epoch=int(100/32)+1,
validation_data=val_dataset,
validation_steps=2)
和模型训练，（输出）种类：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 64) 640
_________________________________________________________________
dense_1 (Dense) (None, 64) 4160
_________________________________________________________________
dense_2 (Dense) (None, 10) 650
=================================================================
Total params: 5,450
Trainable params: 5,450
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
4/4 [==============================] - 0s 69ms/step - loss: 2.3170 - acc: 0.1328 - val_loss: 2.3877 - val_acc: 0.0712
Epoch 2/10
4/4 [==============================] - 0s 2ms/step - loss: 2.2628 - acc: 0.2500 - val_loss: 2.3850 - val_acc: 0.0712
Epoch 3/10
4/4 [==============================] - 0s 2ms/step - loss: 2.2169 - acc: 0.2656 - val_loss: 2.3838 - val_acc: 0.0712
Epoch 4/10
4/4 [==============================] - 0s 2ms/step - loss: 2.1743 - acc: 0.3359 - val_loss: 2.3830 - val_acc: 0.0590
Epoch 5/10
4/4 [==============================] - 0s 2ms/step - loss: 2.1343 - acc: 0.3594 - val_loss: 2.3838 - val_acc: 0.0590
Epoch 6/10
4/4 [==============================] - 0s 2ms/step - loss: 2.0959 - acc: 0.3516 - val_loss: 2.3858 - val_acc: 0.0590
Epoch 7/10
4/4 [==============================] - 0s 4ms/step - loss: 2.0583 - acc: 0.3750 - val_loss: 2.3887 - val_acc: 0.0590
Epoch 8/10
4/4 [==============================] - 0s 2ms/step - loss: 2.0223 - acc: 0.4453 - val_loss: 2.3918 - val_acc: 0.0747
Epoch 9/10
4/4 [==============================] - 0s 2ms/step - loss: 1.9870 - acc: 0.4609 - val_loss: 2.3954 - val_acc: 0.1059
Epoch 10/10
4/4 [==============================] - 0s 2ms/step - loss: 1.9523 - acc: 0.4609 - val_loss: 2.3995 - val_acc: 0.1059
添加回调也可以，
callbacks = [ tf.keras.callbacks.ModelCheckpoint("model_{epoch:04d}_{val_acc:.4f}.h5",
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max'),
tf.keras.callbacks.TensorBoard('./logs'),
tf.keras.callbacks.EarlyStopping(monitor='val_acc', patience=5, min_delta=0, mode='max')
]
model.fit(train_dataset, epochs=10, steps_per_epoch=int(100/32)+1, validation_data=val_dataset,
validation_steps=2, callbacks=callbacks)
输出：
Epoch 1/10
4/4
[==============================] - 0s 59ms/step - loss: 2.3274 - acc: 0.1094 - val_loss: 2.3143 - val_acc: 0.0833
Epoch 00001: val_acc improved from -inf to 0.08333, saving model to model_0001_0.0833.h5
Epoch 2/10
4/4 [==============================] - 0s 2ms/step - loss: 2.2655 - acc: 0.1094 - val_loss: 2.3204 - val_acc: 0.1389
Epoch 00002: val_acc improved from 0.08333 to 0.13889, saving model to model_0002_0.1389.h5
Epoch 3/10
4/4 [==============================] - 0s 5ms/step - loss: 2.2122 - acc: 0.1250 - val_loss: 2.3289 - val_acc: 0.1111
Epoch 00003: val_acc did not improve from 0.13889
Epoch 4/10
4/4 [==============================] - 0s 2ms/step - loss: 2.1644 - acc: 0.1953 - val_loss: 2.3388 - val_acc: 0.0556
Epoch 00004: val_acc did not improve from 0.13889
Epoch 5/10
4/4 [==============================] - 0s 2ms/step - loss: 2.1211 - acc: 0.2734 - val_loss: 2.3495 - val_acc: 0.0556
Epoch 00005: val_acc did not improve from 0.13889
Epoch 6/10
4/4 [==============================] - 0s 4ms/step - loss: 2.0808 - acc: 0.2969 - val_loss: 2.3616 - val_acc: 0.0556
Epoch 00006: val_acc did not improve from 0.13889
Epoch 7/10
4/4 [==============================] - 0s 2ms/step - loss: 2.0431 - acc: 0.2969 - val_loss: 2.3749 - val_acc: 0.0712
Epoch 00007: val_acc did not improve from 0.13889
答案 1 :(得分：0)
我认为您的问题是数据集的类型，其中xt和yt不是列表或数组，因此无法切片或拆分。
假设x是形状为(1000,2)的数组，使用numpy填充随机数，而y是具有10个类的类。
# Define x and y
import numpy as np
np.random.seed(2018)
n_cat = 10
x = np.random.rand(1000,2) # Generate random 2 variables and 1000 rows
y = [np.random.randint(1,n_cat) for i in range(len(x))] # Make 10 class
然后组装模型并进行编译
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'],
)
为检查点模型创建回调
callbacks = [
tf.keras.callbacks.ModelCheckpoint("model_{epoch:04d}_{val_acc:.4f}.h5",
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max'),
tf.keras.callbacks.TensorBoard(os.path.join('.', 'logs')),
tf.keras.callbacks.EarlyStopping(monitor='val_acc', patience=5, min_delta=0, mode='max')
]
拟合模型
model.fit(x, y, batch_size=32, epochs=100, callbacks=callbacks, validation_split=0.1)
对我有用。