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私は TensorFlow の初心者であり、それがどのように機能するかをまだ理解しようとしているため、エラーが私のアーキテクチャの問題なのか、それとももっと基本的なものなのかわかりません。ここでは、シャム ニューラル ネットワークをトレーニングしようとしています (左右の入力を同じ重みで左右の NN にフィードし、入力が類似している場合は距離が小さく、入力が異なる場合は距離が大きい特徴ベクトルにマッピングしようとします)。

私が得るエラーは、回帰ステップで発生します:

  File "siamese.py", line 59, in <module>
    network = regression(y_pred, optimizer='adam',
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tflearn/models/dnn.py", line 63, in __init__
    best_val_accuracy=best_val_accuracy)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tflearn/helpers/trainer.py", line 120, in __init__
    clip_gradients)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tflearn/helpers/trainer.py", line 646, in initialize_training_ops
    ema_num_updates=self.training_steps)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tflearn/summaries.py", line 236, in add_loss_summaries
    loss_averages_op = loss_averages.apply([loss] + other_losses)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/training/moving_averages.py", line 292, in apply
    colocate_with_primary=(var.op.type == "Variable"))
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/training/slot_creator.py", line 106, in create_zeros_slot
    val = array_ops.zeros(primary.get_shape().as_list(), dtype=dtype)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1071, in zeros
    shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 628, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 198, in _tensor_shape_tensor_conversion_function
    "Cannot convert a partially known TensorShape to a Tensor: %s" % s)
ValueError: Cannot convert a partially known TensorShape to a Tensor: (?,)

最初の次元がバッチ サイズである必要がある場合、この問題を解決する方法がわかりませんNone(間違っている場合は修正してください)。

コードの関連部分は次のとおりです。

BATCH_SIZE=100
def contrastive_loss(y_pred, y_true, margin=1.0):
    return tf.mul(1-y_true, tf.square(y_pred)) + tf.mul(y_true, tf.square(tf.maximum((margin-y_pred),0)))

## Load dataset
f = h5py.File('./data/paired_training_data.hdf','r')
X1 = f["train_X1"]
X2 = f["train_X2"]
Y = f["train_Y_paired"]

## Inputs: 1 example (phoneme pair), dropout probability
inp_sound1 = input_data(shape=[None, 1, N_MFCC_CHANNELS, N_IN_CHANNELS])
networkL = conv_1d(inp_sound1, reuse=None, scope="conv1d")
networkL = max_pool_1x6(networkL)
networkL = fully_connected(networkL, n_units=N_FULLY_CONN, activation='relu', scope="fc1")
networkL = dropout(networkL, .5) # unshared?
networkL = fully_connected(networkL, n_units=N_FULLY_CONN, activation='relu', scope="fc2")

inp_sound2 = input_data(shape=[None, 1, N_MFCC_CHANNELS, N_IN_CHANNELS])
networkR = conv_1d(inp_sound2, reuse=True, scope="conv1d")
networkR = max_pool_1x6(networkR)
networkR = fully_connected(networkR, n_units=N_FULLY_CONN, activation='relu', reuse=True, scope="fc1")
networkR = dropout(networkR, .5)
networkR = fully_connected(networkR, n_units=N_FULLY_CONN, activation='relu', reuse=True, scope="fc2")

l2_loss = tf.reduce_sum(tf.square(tf.sub(networkL, networkR)), 1)
y_pred = tf.sqrt(l2_loss)
#y_true = input_data(shape=[None])

## Training
network = regression(y_pred, optimizer='adam',
            loss=contrastive_loss, learning_rate=0.0001, to_one_hot=False)
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit([X1, X2], Y, n_epoch=10, batch_size=BATCH_SIZE, show_metric=True, validation_set=0.1)

特に将来的にこれらの問題を自分でデバッグする方法を理解するための助けがあれば、大歓迎です!

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1 に答える 1

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TensorFlow が の形状を推測できないようですcontrastive_loss。出力形状が事前にわかっている場合は、関数set_shapeを呼び出してみてください。contrastive_loss

def contrastive_loss(y_pred, y_true, margin=1.0):
  loss = tf.mul(1-y_true, tf.square(y_pred)) + tf.mul(y_true, tf.square(tf.maximum((margin-y_pred),0)))
  loss.set_shape([...])
  return loss
于 2016-11-15T20:27:35.250 に答える