Conv Net Tensorflowのレイヤー数：ValueError：新しい変数の形状（logistic_regression/weights）を完全に定義する必要があります

Question

文字レベルの畳み込みNNを構築しています。私は私は私が修正することはできませんエラーを得続けるためtensorflowで寸法を再構築/サイズを変更する方法を大まかに見当もつかないと思う私は、学習データとしてのサンプルの束を持って、各サンプルは、3640の寸法を有する：

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). 

ここでは、コードは次のようになります。

import tensorflow as tf 
from tensorflow.contrib import learn 

N_FEATURES = 140*26 
N_FILTERS = 10 
WINDOW_SIZE = 3 

コンベンションモデルが開始されます。

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()) 

ここでエラーが来る：

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 

の

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) 

メイン：

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

問題がlogistic_regression()にpool引数は、列の既知の数を持っていないということであるように見えます。 linear_regression()は、適切なサイズの重み行列を作成するために、そのx引数の列数を知る必要があります。

この問題は、次の行から茎：

pool_shape = tf.shape(pool) 
pool = tf.reshape(pool, [pool_shape[0], pool_shape[2]*pool_shape[3]]) 

pool_shape[2]*pool_shape[3]が一定値を持っていますが、それはにテンソルpoolの静的な形状を推測するので、TensorFlowのクライアント側定数の畳み込みは、現在、この式を処理しません。 (?, ?)（ログ出力が示すように）です。 tf.TensorShapeオブジェクトではなくtf.Tensorオブジェクトとして、代わりtf.shape(pool)のpool.get_shape()はpoolの（部分的に定義された）形状に関するTensorFlowもう少し情報を与える使用

pool_shape = pool.get_shape() 
pool = tf.reshape(pool, [-1, (pool_shape[2] * pool_shape[3]).value]) 

：一つの回避策は、次の変更を行うことです。この変更後、pool_shape[2]とpool_shape[3]には既知の値があるため、poolの列の数がわかります。