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首页 > 解决方案 > ValueError: 层 conv1_pad 的输入 0 与层不兼容：预期 ndim=4，发现 ndim=2。收到的完整形状：[无，260]

tensorflow - ValueError: 层 conv1_pad 的输入 0 与层不兼容：预期 ndim=4，发现 ndim=2。收到的完整形状：[无，260]

问题描述

我收到多模式问题的错误。输入形状：img 输入：- (3740, 150, 150, 3)，字输入：- (3740, 260) 其中 3740 是样本数。这里已附加模型作为函数，其中 build_img_encoder 描述 IMG 编码器模型， build_wrd_conv 描述 word_encoder 部分，它们是模型图图像中可见的 2 个输入分支

def build_merged_model_Concatenate(embedding_matrix, input_length):#input_length-->word length
    #call encoders
    #define input 2 channels
    img_input = Input(shape= build_img_encoder_model().input_shape[1:], name="img_input")
    wrd_input = Input(shape= build_wrd_conv(embedding_matrix ,input_length).input_shape[1:], name="wrd_input")

    #call img and word encoders
    img_encoder = build_img_encoder_model()(img_input)
    wrd_encoder = build_wrd_conv(embedding_matrix, input_length)(wrd_input)

    #merge 2 models
    merge = concatenate([img_encoder, wrd_encoder], name="concat_merge1")

    merged_model1_concat = keras.models.Model(inputs=[img_input, wrd_input], outputs = merge)## model assign
    dense_merge0 = Dense(1024, activation='relu',name="dense_merge0")(merge)
    dropout_merge0 = Dropout(0.5, name='dropout_merge0')(dense_merge0)

    dense_merge1 = Dense(256, activation='relu', name='dense_merge1')(dropout_merge0)
    dropout_3 = Dropout(0.2,name="dropout_3")(dense_merge1)
    dense_merge2 = Dense(128, activation='relu', name='dense_merge_2')(dropout_3)
    output_classify = Dense( 2, activation='sigmoid')(dense_merge2)
    
    merged_classify = keras.models.Model(inputs=[img_input, wrd_input], outputs= output_classify)## model assign
    
    OPTIMIZER = Adam(learning_rate=0.0001,beta_1=0.9, beta_2=0.999)
    METRICS = ['accuracy',f1_m, precision_m, recall_m]

    merged_model1_concat.compile(loss = 'binary_crossentropy', optimizer=OPTIMIZER, metrics=METRICS)
    merged_classify.compile(loss = 'binary_crossentropy', optimizer=OPTIMIZER, metrics=METRICS)
    return merged_model1_concat, merged_classify

带形状的模型汇总图

在运行模型拟合模型开始训练并运行几乎一个完整的时期，然后出现此错误：-

WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.
WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.
Epoch 1/100
 2/53 [>.............................] - ETA: 6s - loss: 1.0590 - accuracy: 0.6094 - f1_m: 0.5766 - precision_m: 0.6011 - recall_m: 0.5547WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0632s vs `on_train_batch_end` time: 0.1851s). Check your callbacks.
53/53 [==============================] - ETA: 0s - loss: 0.8733 - accuracy: 0.6548 - f1_m: 0.6309 - precision_m: 0.6285 - recall_m: 0.6362WARNING:tensorflow:Model was constructed with shape (None, 150, 150, 3) for input Tensor("img_input_15:0", shape=(None, 150, 150, 3), dtype=float32), but it was called on an input with incompatible shape (None, 260).
WARNING:tensorflow:Model was constructed with shape (None, 150, 150, 3) for input Tensor("input_54:0", shape=(None, 150, 150, 3), dtype=float32), but it was called on an input with incompatible shape (None, 260).
WARNING:tensorflow:Model was constructed with shape (None, 150, 150, 3) for input Tensor("input_53:0", shape=(None, 150, 150, 3), dtype=float32), but it was called on an input with incompatible shape (None, 260).
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-84-23200389ca70> in <module>()
     26     x, concat_merge_classify = build_merged_model_Concatenate(embedding_matrix_full, input_length)
     27     ##plot_model(Classify_Model1, to_file="MergedClassify_Model_2_multiply.png",show_shapes=True)
---> 28     hist2= concat_merge_classify.fit(x=[X_TRAIN_, padded_docs_train] , y=Y_train, epochs=100, batch_size =64,                                      callbacks=[reducelr, checkpointer,earlyStopping],                                      validation_data = ([padded_docs_test],Y_test))
     29     print('\n# Evaluate on test data')
     30     loss, accuracy, f1_score, precision, recall= concat_merge_classify.evaluate([X_TEST_, padded_docs_test], Y_test,                                                                                verbose=1, batch_size=64)

12 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

ValueError: in user code:
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1224 test_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1215 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1208 run_step  **
        outputs = model.test_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1174 test_step
        y_pred = self(x, training=False)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call
        inputs, training=training, mask=mask)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph
        outputs = node.layer(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call
        inputs, training=training, mask=mask)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph
        outputs = node.layer(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call
        inputs, training=training, mask=mask)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph
        outputs = node.layer(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__
        self.name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:180 assert_input_compatibility
        str(x.shape.as_list()))

    ValueError: Input 0 of layer conv1_pad is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [None, 260]

那么发生了什么事？，看起来我在某个地方错过了一些形状。

标签： tensorflowkerasdeep-learningtensorflow2.0multimodal

解决方案

检查这个：

validation_data = ([padded_docs_test],Y_test)).

看起来它与训练数据无关

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