Wie kann ich Formfehler vermeiden, wenn ich Keras Backend von Theano auf Tensorflow ändere?

Question

Ich versuchte Deep Semantic Similarity Model (DSSM) zu verwenden: https://github.com/airalcorn2/Deep-Semantic-Similarity-Model/blob/master/deep_semantic_similarity_keras.py auf Keras mit Tensorflow Backend. Der Code ist für Keras mit Theano-Backend und funktioniert ohne Fehler.

Um es korrekt auf Keras mit Tensorflow Backend laufen zu lassen, habe ich geändert, da einige Tensor-Attribute:

-: backend.dot(x, backend.transpose(y))/(x.norm(2) * y.norm(2)) 
+: backend.dot(backend.l2_normalize(x, 0), backend.transpose(backend.l2_normalize(y, 0))) 

und

-: x.max(axis = 1) 
+: backend.max(x, 1) 

Wenn ich das modifizierte Modell erstellt, Valueerror: Logits und Ziele muss die gleiche Form haben (() vs (?,?)) aufgetreten ist. Wie kann ich den Fehler vermeiden? Hier

ist der modifizierte Code

import keras 
import numpy as np 

from keras import backend 
from keras.layers import Input, merge 
from keras.layers.core import Dense, Lambda, Reshape 
from keras.layers.convolutional import Convolution1D 
from keras.models import Model 

def R(vects): 
    (x, y) = vects 
    return backend.dot(backend.l2_normalize(x, 0), backend.transpose(backend.l2_normalize(y, 0))) 

LETTER_GRAM_SIZE = 3 
WINDOW_SIZE = 3 
TOTAL_LETTER_GRAMS = int(3 * 1e4) 
WORD_DEPTH = WINDOW_SIZE * TOTAL_LETTER_GRAMS 
K = 300 
L = 128 
J = 4 
FILTER_LENGTH = 1 

query = Input(shape = (None, WORD_DEPTH)) 
pos_doc = Input(shape = (None, WORD_DEPTH)) 
neg_docs = [Input(shape = (None, WORD_DEPTH)) for j in range(J)] 

query_conv = Convolution1D(K, FILTER_LENGTH, border_mode = "same", input_shape = (None, WORD_DEPTH), activation = "tanh")(query) # See equation (2). 

query_max = Lambda(lambda x: backend.max(x, 1), output_shape = (K,))(query_conv) 

query_sem = Dense(L, activation = "tanh", input_dim = K)(query_max) 

doc_conv = Convolution1D(K, FILTER_LENGTH, border_mode = "same", input_shape = (None, WORD_DEPTH), activation = "tanh") 
doc_max = Lambda(lambda x: backend.max(x, 1), output_shape = (K,)) 
doc_sem = Dense(L, activation = "tanh", input_dim = K) 

pos_doc_conv = doc_conv(pos_doc) 
neg_doc_convs = [doc_conv(neg_doc) for neg_doc in neg_docs] 

pos_doc_max = doc_max(pos_doc_conv) 
neg_doc_maxes = [doc_max(neg_doc_conv) for neg_doc_conv in neg_doc_convs] 

pos_doc_sem = doc_sem(pos_doc_max) 
neg_doc_sems = [doc_sem(neg_doc_max) for neg_doc_max in neg_doc_maxes] 

R_layer = Lambda(R, output_shape = (1,)) 

R_Q_D_p = R_layer([query_sem, pos_doc_sem]) 
R_Q_D_ns = [R_layer([query_sem, neg_doc_sem]) for neg_doc_sem in neg_doc_sems] 

concat_Rs = merge([R_Q_D_p] + R_Q_D_ns, mode = "concat") 
concat_Rs = Reshape((J + 1, 1))(concat_Rs) 

weight = np.array([1]).reshape(1, 1, 1, 1) 
with_gamma = Convolution1D(1, 1, border_mode = "same", input_shape = (J + 1, 1), activation = "linear", bias = False, weights = [weight])(concat_Rs) 

exponentiated = Lambda(lambda x: backend.exp(x), output_shape = (J + 1,))(with_gamma) # See equation (5). 
exponentiated = Reshape((J + 1,))(exponentiated) 

prob = Lambda(lambda x: x[0][0]/backend.sum(x[0]), output_shape = (1,))(exponentiated) 

model = Model(input = [query, pos_doc] + neg_docs, output = prob) 
model.compile(optimizer = "adadelta", loss = "binary_crossentropy") 

und Fehlermeldung.

--------------------------------------------------------------------------- 
ValueError        Traceback (most recent call last) 
/Users/jun/anaconda/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py in merge_with(self, other) 
    571  try: 
--> 572   self.assert_same_rank(other) 
    573   new_dims = [] 

/Users/jun/anaconda/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py in assert_same_rank(self, other) 
    617   raise ValueError(
--> 618    "Shapes %s and %s must have the same rank" % (self, other)) 
    619 

ValueError: Shapes (?, ?) and() must have the same rank 

During handling of the above exception, another exception occurred: 

ValueError        Traceback (most recent call last) 
/Users/jun/anaconda/lib/python3.5/site-packages/tensorflow/python/ops/nn.py in sigmoid_cross_entropy_with_logits(logits, targets, name) 
    430  try: 
--> 431  targets.get_shape().merge_with(logits.get_shape()) 
    432  except ValueError: 

/Users/jun/anaconda/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py in merge_with(self, other) 
    578   raise ValueError("Shapes %s and %s are not compatible" % 
--> 579       (self, other)) 
    580 

ValueError: Shapes (?, ?) and() are not compatible 

During handling of the above exception, another exception occurred: 

ValueError        Traceback (most recent call last) 
<ipython-input-6-a4001289be07> in <module>() 
    61 
    62 model = Model(input = [query, pos_doc] + neg_docs, output = prob) 
---> 63 model.compile(optimizer = "adadelta", loss = "binary_crossentropy") 

/Users/jun/anaconda/lib/python3.5/site-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, **kwargs) 
    628    loss_weight = loss_weights_list[i] 
    629    output_loss = weighted_loss(y_true, y_pred, 
--> 630           sample_weight, mask) 
    631    if len(self.outputs) > 1: 
    632     self.metrics_tensors.append(output_loss) 

/Users/jun/anaconda/lib/python3.5/site-packages/keras/engine/training.py in weighted(y_true, y_pred, weights, mask) 
    330  def weighted(y_true, y_pred, weights, mask=None): 
    331   # score_array has ndim >= 2 
--> 332   score_array = fn(y_true, y_pred) 
    333   if mask is not None: 
    334    # Cast the mask to floatX to avoid float64 upcasting in theano 

/Users/jun/anaconda/lib/python3.5/site-packages/keras/objectives.py in binary_crossentropy(y_true, y_pred) 
    46 
    47 def binary_crossentropy(y_true, y_pred): 
---> 48  return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1) 
    49 
    50 

/Users/jun/anaconda/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in binary_crossentropy(output, target, from_logits) 
    1464   output = tf.clip_by_value(output, epsilon, 1 - epsilon) 
    1465   output = tf.log(output/(1 - output)) 
-> 1466  return tf.nn.sigmoid_cross_entropy_with_logits(output, target) 
    1467 
    1468 

/Users/jun/anaconda/lib/python3.5/site-packages/tensorflow/python/ops/nn.py in sigmoid_cross_entropy_with_logits(logits, targets, name) 
    432  except ValueError: 
    433  raise ValueError("logits and targets must have the same shape (%s vs %s)" 
--> 434      % (logits.get_shape(), targets.get_shape())) 
    435 
    436  # The logistic loss formula from above is 

ValueError: logits and targets must have the same shape (() vs (?, ?)) 

Answer 1

(Umgerechnet aus einem Kommentar zu einer Antwort auf Wunsch des @ DGA)

ich unsere Resident Keras Experten gefragt, und seine Antwort war: bei der Fehlermeldung und Code-Suche, kann man, dass die endgültigen schließen Ausgabeprob ist ein Skalar, wohingegen es ein 2D-Array sein sollte (eine Skalarwahrscheinlichkeit pro Batch-Eintrag). Das Problem ist wahrscheinlich auf Linie sein

prob = Lambda(lambda x: x[0][0]/backend.sum(x[0]), output_shape = (1,))(exponentiated)