我正在尝试使用 TFRecordDataset
训练 CNN(我认为这是无关的,但这是我的情况)并收到以下错误:
ValueError: 维度 0 的切片索引 0 越界。对于'{{节点 strided_slice}} = StridedSlice[索引=DT_INT32,T=DT_INT32, begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, shrink_axis_mask=1](形状,strided_slice/stack,strided_slice/stack_1, strided_slice/stack_2)' 输入形状:[0], [1], [1], [1] 和 计算输入张量:input[1] = <0>, input[2] = <1>, input[3] = <1>.
举个例子,这是我正在执行的代码:
美国有线电视新闻网:
import tensorflow as tf
def get_cnn_model(input_shape=(31, 31, 9), n_outputs=4, convolutions=3, optimizer='adam', seed=26):
tf.random.set_seed(seed=seed)
_input = layers.Input(shape=input_shape, name='input')
x = layers.Conv2D(64, (4, 4), activation='relu', padding='same', name=f'conv_0')(_input)
x = layers.MaxPooling2D(2)(x)
for i in range(convolutions - 1):
x = layers.Conv2D(64, (4, 4), activation='relu', padding='same', name=f'conv_{i + 1}')(x)
x = layers.MaxPooling2D(2)(x)
x = layers.Flatten()(x)
x = layers.Dense(128, activation='relu', name='dense_1')(x)
x = layers.Dropout(0.35, name='dropout_1')(x)
x = layers.Dense(128, activation='relu', name='dense_2')(x)
x = layers.Dropout(0.35, name='dropout_2')(x)
p = layers.Dense(n_outputs, activation='tanh', name='p')(x)
v = layers.Dense(1, activation='tanh', name='v')(x)
cnn_model = Model(inputs=_input, outputs=[v, p])
losses = {
"v": 'mean_squared_error',
"p": keras.losses.BinaryCrossentropy()
}
cnn_model.compile(loss=losses, optimizer=optimizer)
return cnn_model
cnn = get_cnn_model((31, 31, 9), n_outputs=16, convolutions=3, optimizer='adam', seed=26)
这是示例数据集:
import numpy as np
import tensorflow as tf
v = 0.9
p = np.random.randn(16)
state = np.random.randn(31*31*9)
sample = tf.train.Example(
features = tf.train.Features(
feature = {
'v': tf.train.Feature(float_list=tf.train.FloatList(value=[v])),
'p': tf.train.Feature(float_list=tf.train.FloatList(value = p)),
's': tf.train.Feature(float_list=tf.train.FloatList(value = state))
}
)
)
with tf.io.TFRecordWriter('tf_record_data') as f:
f.write(sample.SerializeToString())
这是我得到上述错误的训练过程:
def read_tfrecord(example):
feature_desc = {
'v': tf.io.FixedLenFeature([], tf.float32),
'p': tf.io.VarLenFeature(tf.float32),
's': tf.io.VarLenFeature(tf.float32)
}
sample = tf.io.parse_single_example(example, feature_desc)
x = tf.reshape(tf.sparse.to_dense(parsed['s']), (1,31,31, 9))
y = {'v':sample['v'], 'p': tf.sparse.to_dense(sample['p'])}
return x, y
ds = tf.data.TFRecordDataset(['tf_record_data'])
ds = ds.map(read_tfrecord)
cnn.fit(ds)
有趣的是,当我对数据集进行预测时,它确实有效:
import numpy as np
for serialized in tf.data.TFRecordDataset(['tf_record_data']):
parsed = tf.io.parse_single_example(serialized, feature_desc)
st= tf.sparse.to_dense(parsed['s'])
t = tf.reshape(st, (1, 31, 31, 9))
print(cnn.predict(t))
我该如何解决这个错误?
答案 0 :(得分:0)
我将数据记录的映射更改为以下内容:
def read_tfrecord(example):
feature_desc = {
'v': tf.io.FixedLenFeature([], tf.float32),
'p': tf.io.VarLenFeature(tf.float32),
's': tf.io.VarLenFeature(tf.float32)
}
sample = tf.io.parse_single_example(example, feature_desc)
x = tf.reshape(tf.sparse.to_dense(parsed['s']), (1,rows,cols, layers))
p = tf.reshape(tf.sparse.to_dense(parsed['p']), (1, 16))
v = tf.reshape(sample['v'], (1, 1))
y = {'v':v, 'p': p}
return x, y
重塑输出解决了问题