Dimentions of layers in Conv Net Tensorflow: ValueError: Shape of a new variable (logistic_regression/weights) must be fully defined
Dimentions of layers in Conv Net Tensorflow: ValueError: Shape of a new variable (logistic_regression/weights) must be fully defined
我正在构建字符级卷积神经网络。我有一堆样本作为训练数据，每个样本的维度都是 3640。我想我几乎不知道如何在 tensorflow 中 resize/reshape 维度，因为我不断收到无法修复的错误：
Traceback (most recent call last):
File "/Users/osopova/Documents/00_KSU_Masters/00_2016_Fall/00_Research/cnn_da/step_4_cnn_4.py", line 87, in my_conv_model
prediction, loss = learn.models.logistic_regression(pool, y)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/models.py", line 146, in logistic_regression
'weights', [x.get_shape()[1], y.get_shape()[-1]], dtype=dtype)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 873, in get_variable
custom_getter=custom_getter)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 700, in get_variable
custom_getter=custom_getter)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 217, in get_variable
validate_shape=validate_shape)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 202, in _true_getter
caching_device=caching_device, validate_shape=validate_shape)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 515, in _get_single_variable
"but instead was %s." % (name, shape))
ValueError: Shape of a new variable (logistic_regression/weights) must be fully defined, but instead was (?, 1).
Traceback (most recent call last):
File "/Users/osopova/Documents/00_KSU_Masters/00_2016_Fall/00_Research/cnn_da/step_4_cnn_4.py", line 175, in <module>
Traceback (most recent call last):
File "/Users/osopova/Documents/00_KSU_Masters/00_2016_Fall/00_Research/cnn_da/step_4_cnn_4.py", line 87, in my_conv_model
prediction, loss = learn.models.logistic_regression(pool, y)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/models.py", line 146, in logistic_regression
'weights', [x.get_shape()[1], y.get_shape()[-1]], dtype=dtype)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 873, in get_variable
custom_getter=custom_getter)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 700, in get_variable
custom_getter=custom_getter)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 217, in get_variable
validate_shape=validate_shape)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 202, in _true_getter
caching_device=caching_device, validate_shape=validate_shape)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 515, in _get_single_variable
"but instead was %s." % (name, shape))
ValueError: Shape of a new variable (logistic_regression/weights) must be fully defined, but instead was (?, 1).
代码如下：
import tensorflow as tf
from tensorflow.contrib import learn
N_FEATURES = 140*26
N_FILTERS = 10
WINDOW_SIZE = 3
转换模型开始：
def my_conv_model(x, y):
# to form a 4d tensor of shape batch_size x 1 x N_FEATURES x 1
x = tf.reshape(x, [-1, 1, N_FEATURES, 1])
# this will give sliding window of 1 x WINDOW_SIZE convolution.
features = tf.contrib.layers.convolution2d(inputs=x,
num_outputs=N_FILTERS,
kernel_size=[1, WINDOW_SIZE],
padding='VALID')
# Add a RELU for non linearity.
features = tf.nn.relu(features)
# Max pooling across output of Convolution+Relu.
pool = tf.nn.max_pool(features, ksize=[1, 1, 2, 1],
strides=[1, 1, 2, 1], padding='SAME')
print("(1) pool_shape", pool.get_shape())
print("(1) y_shape", y.get_shape())
pool_shape = tf.shape(pool)
pool = tf.reshape(pool, [pool_shape[0], pool_shape[2]*pool_shape[3]])
y = tf.expand_dims(y, 1)
print("(2) pool_shape", pool.get_shape())
print("(2) y_shape", y.get_shape())
try:
exc_info = sys.exc_info()
print("(3) pool_shape", pool.get_shape())
print("(3) y_shape", y.get_shape())
错误来了：
prediction, loss = learn.models.logistic_regression(pool, y)
return prediction, loss
except Exception:
#print(traceback.format_exc())
pass
finally:
# Display the *original* exception
traceback.print_exception(*exc_info)
del exc_info
#return prediction, loss
形状：
(1) pool_shape (?, 1, 1819, 10)
(1) y_shape (?,)
(2) pool_shape (?, ?)
(2) y_shape (?, 1)
(3) pool_shape (?, ?)
(3) y_shape (?, 1)
主要：
def main(unused_argv):
# training and testing data encoded as one-hot
data_folder = './data'
sandyData = np.loadtxt(data_folder+'/sandyData.csv', delimiter=',')
sandyLabels = np.loadtxt(data_folder+'/sandyLabels.csv', delimiter=',')
x_train, x_test, y_train, y_test = \
train_test_split(sandyData, sandyLabels, test_size=0.2, random_state=7)
x_train = np.array(x_train, dtype=np.float32)
x_test = np.array(x_test, dtype=np.float32)
y_train = np.array(y_train, dtype=np.float32)
y_test = np.array(y_test, dtype=np.float32)
# Build model
classifier = learn.Estimator(model_fn=my_conv_model)
# Train and predict
classifier.fit(x_train, y_train, steps=100)
y_predicted = [p['class'] for p in classifier.predict(x_test, as_iterable=True)]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
if __name__ == '__main__':
tf.app.run() `
看起来问题是 logistic_regression() 的 pool 参数没有已知的列数。 linear_regression() 需要知道其 x 参数中的列数以创建适当大小的权重矩阵。
此问题源于以下行：
pool_shape = tf.shape(pool)
pool = tf.reshape(pool, [pool_shape[0], pool_shape[2]*pool_shape[3]])
虽然 pool_shape[2]*pool_shape[3] 有一个常量值，但 TensorFlow 的客户端常量折叠目前不处理这个表达式，因此它推断张量 pool 的静态形状为 (?, ?) （如您的日志输出所示）。一种解决方法是进行以下更改：
pool_shape = pool.get_shape()
pool = tf.reshape(pool, [-1, (pool_shape[2] * pool_shape[3]).value])
使用 pool.get_shape() instead of tf.shape(pool) 为 TensorFlow 提供了更多关于 pool 的（部分定义的）形状的信息，作为 tf.TensorShape 对象而不是 tf.Tensor 对象。此更改后，pool_shape[2] 和 pool_shape[3] 都有已知值，因此 pool 中的列数将是已知的。
我正在构建字符级卷积神经网络。我有一堆样本作为训练数据，每个样本的维度都是 3640。我想我几乎不知道如何在 tensorflow 中 resize/reshape 维度，因为我不断收到无法修复的错误：
Traceback (most recent call last):
File "/Users/osopova/Documents/00_KSU_Masters/00_2016_Fall/00_Research/cnn_da/step_4_cnn_4.py", line 87, in my_conv_model
prediction, loss = learn.models.logistic_regression(pool, y)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/models.py", line 146, in logistic_regression
'weights', [x.get_shape()[1], y.get_shape()[-1]], dtype=dtype)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 873, in get_variable
custom_getter=custom_getter)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 700, in get_variable
custom_getter=custom_getter)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 217, in get_variable
validate_shape=validate_shape)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 202, in _true_getter
caching_device=caching_device, validate_shape=validate_shape)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 515, in _get_single_variable
"but instead was %s." % (name, shape))
ValueError: Shape of a new variable (logistic_regression/weights) must be fully defined, but instead was (?, 1).
Traceback (most recent call last):
File "/Users/osopova/Documents/00_KSU_Masters/00_2016_Fall/00_Research/cnn_da/step_4_cnn_4.py", line 175, in <module>
Traceback (most recent call last):
File "/Users/osopova/Documents/00_KSU_Masters/00_2016_Fall/00_Research/cnn_da/step_4_cnn_4.py", line 87, in my_conv_model
prediction, loss = learn.models.logistic_regression(pool, y)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/models.py", line 146, in logistic_regression
'weights', [x.get_shape()[1], y.get_shape()[-1]], dtype=dtype)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 873, in get_variable
custom_getter=custom_getter)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 700, in get_variable
custom_getter=custom_getter)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 217, in get_variable
validate_shape=validate_shape)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 202, in _true_getter
caching_device=caching_device, validate_shape=validate_shape)
File "/Users/osopova/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 515, in _get_single_variable
"but instead was %s." % (name, shape))
ValueError: Shape of a new variable (logistic_regression/weights) must be fully defined, but instead was (?, 1).
代码如下：
import tensorflow as tf
from tensorflow.contrib import learn
N_FEATURES = 140*26
N_FILTERS = 10
WINDOW_SIZE = 3
转换模型开始：
def my_conv_model(x, y):
# to form a 4d tensor of shape batch_size x 1 x N_FEATURES x 1
x = tf.reshape(x, [-1, 1, N_FEATURES, 1])
# this will give sliding window of 1 x WINDOW_SIZE convolution.
features = tf.contrib.layers.convolution2d(inputs=x,
num_outputs=N_FILTERS,
kernel_size=[1, WINDOW_SIZE],
padding='VALID')
# Add a RELU for non linearity.
features = tf.nn.relu(features)
# Max pooling across output of Convolution+Relu.
pool = tf.nn.max_pool(features, ksize=[1, 1, 2, 1],
strides=[1, 1, 2, 1], padding='SAME')
print("(1) pool_shape", pool.get_shape())
print("(1) y_shape", y.get_shape())
pool_shape = tf.shape(pool)
pool = tf.reshape(pool, [pool_shape[0], pool_shape[2]*pool_shape[3]])
y = tf.expand_dims(y, 1)
print("(2) pool_shape", pool.get_shape())
print("(2) y_shape", y.get_shape())
try:
exc_info = sys.exc_info()
print("(3) pool_shape", pool.get_shape())
print("(3) y_shape", y.get_shape())
prediction, loss = learn.models.logistic_regression(pool, y)
return prediction, loss
except Exception:
#print(traceback.format_exc())
pass
finally:
# Display the *original* exception
traceback.print_exception(*exc_info)
del exc_info
#return prediction, loss
形状：
(1) pool_shape (?, 1, 1819, 10)
(1) y_shape (?,)
(2) pool_shape (?, ?)
(2) y_shape (?, 1)
(3) pool_shape (?, ?)
(3) y_shape (?, 1)
主要：
def main(unused_argv):
# training and testing data encoded as one-hot
data_folder = './data'
sandyData = np.loadtxt(data_folder+'/sandyData.csv', delimiter=',')
sandyLabels = np.loadtxt(data_folder+'/sandyLabels.csv', delimiter=',')
x_train, x_test, y_train, y_test = \
train_test_split(sandyData, sandyLabels, test_size=0.2, random_state=7)
x_train = np.array(x_train, dtype=np.float32)
x_test = np.array(x_test, dtype=np.float32)
y_train = np.array(y_train, dtype=np.float32)
y_test = np.array(y_test, dtype=np.float32)
# Build model
classifier = learn.Estimator(model_fn=my_conv_model)
# Train and predict
classifier.fit(x_train, y_train, steps=100)
y_predicted = [p['class'] for p in classifier.predict(x_test, as_iterable=True)]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: {0:f}'.format(score))
if __name__ == '__main__':
tf.app.run() `
看起来问题是 logistic_regression() 的 pool 参数没有已知的列数。 linear_regression() 需要知道其 x 参数中的列数以创建适当大小的权重矩阵。
此问题源于以下行：
pool_shape = tf.shape(pool)
pool = tf.reshape(pool, [pool_shape[0], pool_shape[2]*pool_shape[3]])
虽然 pool_shape[2]*pool_shape[3] 有一个常量值，但 TensorFlow 的客户端常量折叠目前不处理这个表达式，因此它推断张量 pool 的静态形状为 (?, ?) （如您的日志输出所示）。一种解决方法是进行以下更改：
pool_shape = pool.get_shape()
pool = tf.reshape(pool, [-1, (pool_shape[2] * pool_shape[3]).value])
使用 pool.get_shape() instead of tf.shape(pool) 为 TensorFlow 提供了更多关于 pool 的（部分定义的）形状的信息，作为 tf.TensorShape 对象而不是 tf.Tensor 对象。此更改后，pool_shape[2] 和 pool_shape[3] 都有已知值，因此 pool 中的列数将是已知的。