我试图以以下方式创建一个输入:
Tx = 318 n_freq = 101 input_anchor = Input(shape=(n_freq,Tx), name='input_anchor')当我运行:
input_anchor.shape我得到:
TensorShape([None, 101, 318])后来当我尝试在模型中使用该输入时，我得到了以下错误:
TypeError: Cannot iterate over a tensor with unknown first dimension.在TensorFlow的opy.py中，我找到了这个代码块，很可能就是我的代码出错的地方:
def __iter__(self): if not context.executing_eagerly(): raise TypeError( "Tensor objects are only iterable when eager execution is " "enabled. To iterate over this tensor use tf.map_fn.") shape = self._shape_tuple() if shape is None: raise TypeError("Cannot iterate over a tensor with unknown shape.") if not shape: raise TypeError("Cannot iterate over a scalar tensor.") if shape[0] is None: raise TypeError( "Cannot iterate over a tensor with unknown first dimension.") for i in xrange(shape[0]): yield self[i]如果你想查看我的整个模型实现，这里是:
def base_model(input_shape): X_input = Input(shape = input_shape) # Step 1: CONV layer (≈4 lines) X = Conv1D(196,kernel_size = 15, strides = 4)(X_input) # CONV1D X = BatchNormalization()(X) # Batch normalization X = Activation('relu')(X) # ReLu activation X = Dropout(rate = 0.2)(X) # dropout (use 0.8) # Step 2: First GRU Layer (≈4 lines) X = LSTM(units = 128, return_sequences = True)(X_input) # GRU (use 128 units and return the sequences) X = Dropout(rate = 0.2)(X) # dropout (use 0.8) X = BatchNormalization()(X) # Batch normalization # Step 3: Second GRU Layer (≈4 lines) X = LSTM(units = 128, return_sequences = True)(X) # GRU (use 128 units and return the sequences) X = Dropout(rate = 0.2)(X) # dropout (use 0.8) X = BatchNormalization()(X) # Batch normalization X = Dropout(rate = 0.2)(X) # dropout (use 0.8) # Step 4: Third GRU Layer (≈4 lines) X = LSTM(units = 128)(X) # GRU (use 128 units and return the sequences) X = Dropout(rate = 0.2)(X) # dropout (use 0.8) X = BatchNormalization()(X) # Batch normalization X = Dropout(rate = 0.2)(X) # dropout (use 0.8) X = Dense(64)(X) base_model = Model(inputs = X_input, outputs = X) return base_model def speech_model(input_shape, base_model): #get triplets vectors input_anchor = Input(shape=input_shape, name='input_anchor') input_positive = Input(shape=input_shape, name='input_positive') input_negative = Input(shape=input_shape, name='input_negative') vec_anchor = base_model(input_anchor) vec_positive = base_model(input_positive) vec_negative = base_model(input_negative) #Concatenate vectors vec_positive, vec_negative concat_layer = concatenate([vec_anchor,vec_positive,vec_negative], axis = -1, name='concat_layer') model = Model(inputs = [input_anchor,input_positive,input_negative], outputs = concat_layer, name = 'speech_to_vec') #model = Model(inputs = [input_anchor,input_positive,input_negative], outputs = [vec_anchor,vec_positive,vec_negative], name = 'speech_to_vec') #model = Model(inputs = [input_anchor,input_positiv], outputs=vec_anchor) return model 以及导致错误的那一行:
speech_model = speech_model(input_shape = (n_freq, Tx), base_model = base_model)非常感谢您的阅读，任何帮助解决这个问题的建议都将不胜感激。
回答：
您的base_model(input_shape)函数需要传递tuple，但您传递的是Input Layer。
# 修改vec_anchor = base_model(input_anchor)vec_positive = base_model(input_positive)vec_negative = base_model(input_negative)# 为vec_anchor = base_model(input_shape)vec_positive = base_model(input_shape)vec_negative = base_model(input_shape)此外，您需要修正最终模型的输入和输出，因为concatenate不能连接多个模型类型。
concat_layer = concatenate([vec_anchor.output,vec_positive.output,vec_negative.output], axis = -1, name='concat_layer')model = Model(inputs = [vec_anchor.input,vec_positive.input,vec_negative.input], outputs = concat_layer, name = 'speech_to_vec')