Conv Net Tensorflow 中层的维度：ValueError：必须完全定义新变量的形状（逻辑回归/权重）

Question

我正在构建一个字符级卷积神经网络。我有一堆样本作为训练数据，每个样本的尺寸为 3640。我想我大致不知道如何在 tensorflow 中调整尺寸/重塑尺寸，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

Conv 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() `

Answer 1

看起来问题在于logistic_regression() 的pool 参数没有已知数量的列。 linear_regression() 需要知道其 x 参数中的列数才能创建适当大小的权重矩阵。

这个问题源于以下一行：

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 的客户端常量折叠当前不处理此表达式，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() 而不是tf.shape(pool) 可以为TensorFlow 提供更多关于pool 形状（部分定义）的信息，作为tf.TensorShape 对象而不是tf.Tensor 对象。更改后，pool_shape[2] 和 pool_shape[3] 都具有已知值，因此 pool 中的列数将是已知的。