ValueError：形状 (None, 50) 和 (None, 1) 在 Tensorflow 和 Colab 中不兼容

Question

我正在使用 LSTM 训练 Tensorflow model 以进行预测性维护。 对于每个实例，我创建一个矩阵 (50,4)，其中 50 是历史序列的长度，4 是每个记录的特征数，因此为了训练 model，我使用例如 (55048, 50, 4) 张量和a (55048, 1) 作为标签。 当我在计算机上使用 Jupyter 进行训练时，它可以工作（非常慢，但可以），但在 Colab 上我收到此错误：

Training data shape is (55048, 50, 4)
Labels shape is (55048, 1)
WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, 50, 100)           42000     
_________________________________________________________________
dense (Dense)                (None, 50, 1)             101       
=================================================================
Total params: 42,101
Trainable params: 42,101
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
        outputs = self.distribute_strategy.run(
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 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:2290 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:2649 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:543 train_step  **
        self.compiled_metrics.update_state(y, y_pred, sample_weight)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:406 update_state
        metric_obj.update_state(y_t, y_p)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated
        update_op = update_state_fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/metrics.py:2083 update_state
        label_weights=label_weights)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:351 update_confusion_matrix_variables
        y_pred.shape.assert_is_compatible_with(y_true.shape)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (None, 50) and (None, 1) are incompatible

我与您分享一些代码。 我知道它很长：

def build_lstm(train_data, train_labels, structure=(100,), epochs=50, activation_fun="relu", dropout_rate=0.1,
             loss_function="binary_crossentropy", optimizer="adagrad", val_split=0.2, seq_length=50):
    #n_features = len(train_data.columns)
    print("Train data is\n",train_data)
    acceptable_ids = [idx for idx in train_data['id'].unique() if train_data[train_data['id']==idx].shape[0]>seq_length]
    seq_gen = [list(gen_sequence(train_data[train_data['id']==idx], seq_length)) for idx in acceptable_ids]
    print("Seq gen is\n")
    print(np.array(seq_gen).shape)
    seq_array = np.concatenate(seq_gen,0).astype(np.float32)
    print("Training data shape is", seq_array.shape)
    #train_labels = np.asarray(train_labels).astype('float32').reshape((-1,1))
    label_gen = [gen_labels(train_labels[train_labels['id']==idx], seq_length) for idx in acceptable_ids]
    label_array = np.concatenate(label_gen).astype(np.float32)
    print("Labels shape is", label_array.shape)
    first_layer=True
    model = tf.keras.Sequential()
    for layer_nodes in structure:
        if first_layer:
            model.add(LSTM(layer_nodes, activation=activation_fun, input_shape=(seq_length,train_data.shape[1]-1),
                           dropout=dropout_rate, return_sequences=True))
            first_layer=False
        else:
            model.add(LSTM(layer_nodes, activation=activation_fun, 
                           dropout=dropout_rate, return_sequences=False))
    model.add(Dense(1, activation='sigmoid'))
    model.summary()
    model.compile(loss=loss_function, 
                       optimizer=optimizer, 
                       metrics=['AUC','accuracy'])
    history = model.fit(seq_array,label_array, epochs=epochs, shuffle=True, validation_split=val_split, callbacks=[earlystop_callback])
    return model    
def gen_sequence(id_df, seq_length):
    """ Only sequences that meet the window-length are considered, no padding is used. This means for testing
    we need to drop those which are below the window-length. An alternative would be to pad sequences so that
    we can use shorter ones """
    # for one id I put all the rows in a single matrix
    data_matrix = id_df.drop("id",1).values
    num_elements = data_matrix.shape[0]
    # Iterate over two lists in parallel.
    # For example id1 have 192 rows and sequence_length is equal to 50
    # so zip iterate over two following list of numbers (0,112),(50,192)
    # 0 50 -> from row 0 to row 50
    # 1 51 -> from row 1 to row 51
    # 2 52 -> from row 2 to row 52
    # ...
    # 111 191 -> from row 111 to 191
    for start, stop in zip(range(0, num_elements-seq_length), range(seq_length, num_elements)):
        #print(data_matrix[start:stop, :],"\n")
        yield data_matrix[start:stop, :] 
def gen_labels(id_df, seq_length):
    data_array = id_df.drop("id",1).values
    num_elements = data_array.shape[0]
    return data_array[seq_length:num_elements, :]

...
for comb_hyp in hyp_combinations:
        for id_validation in training_folds_2:
            print(id_validation)
            ## SEPARATE TRAINING SET AND VALIDATION SET
            X_val = X[X.id.isin(id_validation)].copy() 
            X_train = X[~X.id.isin(id_validation)].copy()  
            y_val = y[y.id.isin(id_validation)].copy() 
            y_train = y[~y.id.isin(id_validation)].copy()  
            ## TRAIN THE CLASSIFIER
            clf = build_lstm(train_data=X_train, train_labels=y_train, structure=comb_hyp[2], epochs=EPOCHS, activation_fun=comb_hyp[0], optimizer=SOLVER, seq_length=SEQ_LENGTH)
...

为什么它在 Jupyter 中有效，而在 Colab 中无效？ 感谢您的关注。

Answer 1

就我而言，我卸载tensorflow然后安装了tensorflow-gpu问题就解决了

Answer 2

我已经将运行时设置为 GPU。 如果我将最后一层不是具有一个节点的密集层（用于二进制分类）而是具有一个节点的 LSTM 层作为最后一层，它就可以工作。 也许是因为 LSTM 和 Dense 不应该混用。 谢谢您的回复。