ValueError: Cannot feed value of shape (256, 9, 129) for Tensor 'model/inputs:0', which has shape
问题描述
- speech-enhancement工程 [https://github.com/jtkim-kaist/Speech-enhancement]
- 使用环境
- tensorflow 1.7
- librosa
- matlab2017a
- tensorboardX
当我运行main.py时，发生以下错误。此时，使用的模型是fnn。 后来，当我在trnmodle.py中检查fnn模型时，我发现了错误的原因。 但是，我没有修改datareader.py中的参数或命令。当我使用lstm模型时发生了同样的错误。
ValueError: Cannot feed value of shape (256, 9, 129, 1) for Tensor ‘model/inputs:0’, which has shape ‘(?, 129, 1, 1)’
调用的fnn模型的为 config.py ,如下所示：
import numpy as np
import os
mode = "fnn" # fnn, fcn, lstm, sfnn, irm, tsn
if mode == 'lstm':
time_width = int(16)
else:
time_width = int(9)
fs = float(8000)
win_size = int(0.025 * fs) # The number of samples in window
win_step = int(0.010 * fs)
# nfft = np.int(2 ** (np.floor(np.log2(win_size) + 1)))
nfft = np.int(256)
freq_size = int(nfft/2+1)
lr = 0.0001
lrDecayRate = .99 # 0.99
lrDecayFreq = 2000
keep_prob = 0.9
global_std = 1.18
device = '/gpu:0'
# logs_dir = os.path.abspath('../logs')
dist_num = int(4)
max_epoch = int(1e6)
batch_size = int(256)
test_batch_size = 128
val_step = int(500)
summary_step = int(1000) # 3000
summary_fnum = int(5)
parallel = False
此时trnmodel.py里面的部分代码如下：
def inference(self, inputs):
if config.mode is "fcn":
fm = utils.conv_with_bn(inputs, out_channels=12, filter_size=[config.time_width, 13],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_1")
fm = utils.conv_with_bn(fm, out_channels=16, filter_size=[config.time_width, 11],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_2")
fm = utils.conv_with_bn(fm, out_channels=20, filter_size=[config.time_width, 9],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_3")
fm_skip = utils.conv_with_bn(fm, out_channels=24, filter_size=[config.time_width, 7],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_4")
fm = utils.conv_with_bn(fm_skip, out_channels=32, filter_size=[config.time_width, 7],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_5")
fm = utils.conv_with_bn(fm, out_channels=24, filter_size=[config.time_width, 7],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_6") + fm_skip
fm = utils.conv_with_bn(fm, out_channels=20, filter_size=[config.time_width, 9],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_7")
fm = utils.conv_with_bn(fm, out_channels=16, filter_size=[config.time_width, 11],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_8")
fm = utils.conv_with_bn(fm, out_channels=12, filter_size=[config.time_width, 13],
stride=1, act='relu', is_training=self._is_training,
padding="SAME", name="conv_9")
fm = utils.conv_with_bn(fm, out_channels=1, filter_size=[config.time_width, config.freq_size],
stride=1, act='linear', is_training=self._is_training,
padding="SAME", name="conv_10") # (batch_size, 1, config.freq_size, 1)
# fm = utils.conv_with_bn(fm, out_channels=1, filter_size=[config.time_width, 1],
# stride=1, act='linear', is_training=self._is_training,
# padding="VALID", name="conv_last")
fm = tf.squeeze(fm, [1, 3])
return fm
elif config.mode is "fnn":
keep_prob = self.keep_prob
# inputs = tf.reshape(tf.squeeze(inputs, [3]), (-1, int(config.time_width*config.freq_size)))
# inputs = tf.nn.dropout(inputs, keep_prob=keep_prob)
#
# h1 = tf.nn.relu(utils.batch_norm_affine_transform(inputs, 2048, name='hidden_1',
# is_training=self._is_training))
# # h1 = tf.nn.dropout(h1, keep_prob=keep_prob)
#
# h2 = tf.nn.relu(utils.batch_norm_affine_transform(h1, 2048, name='hidden_2',
# is_training=self._is_training))
# # h2 = tf.nn.dropout(h2, keep_prob=keep_prob)
#
# # h3 = tf.nn.relu(utils.batch_norm_affine_transform(h2, 2048, name='hidden_3',
# # is_training=self._is_training))
# # h3 = tf.nn.dropout(h3, keep_prob=keep_prob)
#
# fm = utils.affine_transform(h2, config.freq_size, name='logits')
inputs = tf.reshape(tf.squeeze(inputs, [3]), (-1, int(config.time_width*config.freq_size)))
inputs = tf.nn.dropout(inputs, keep_prob=keep_prob)
h1 = tf.nn.selu(utils.affine_transform(inputs, 2048, name='hidden_1'))
h1 = tf.nn.dropout(h1, keep_prob=keep_prob)
h2 = tf.nn.selu(utils.affine_transform(h1, 2048, name='hidden_2'))
h2 = tf.nn.dropout(h2, keep_prob=keep_prob)
h3 = tf.nn.selu(utils.affine_transform(h2, 2048, name='hidden_3'))
h3 = tf.nn.dropout(h3, keep_prob=keep_prob)
fm = utils.affine_transform(h3, config.freq_size, name='logits')
return fm
经过分析错误的原因在于：
inputs = tf.reshape(tf.squeeze(inputs, [3]), (-1, int(config.time_width*config.freq_size)))
这里使用的 tf.reshape() ,其中 config.time_width=9 ,config.freq_size=129;
而输入的数据集所采样得到的数据的大小无被 9*129=1161 所整除
方法
目前正在检查输入数据的状况。