ValueError：形状 (None, 9) 和 (None, 10) 不兼容

Question

我在预测站点上有一个包含 565 个特征和 10 个不同列的数据集，用于预测训练 model 中的标签。这是 model 汇总尺寸：

_________________________________________________________________
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv1d (Conv1D)              (None, 564, 64)           256
_________________________________________________________________
flatten (Flatten)            (None, 36096)             0
_________________________________________________________________
dense (Dense)                (None, 50)                1804850
_________________________________________________________________
dense_1 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_2 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_3 (Dense)              (None, 50)                2550
_________________________________________________________________
dense_4 (Dense)              (None, 10)                510
=================================================================
Total params: 1,813,266
Trainable params: 1,813,266
Non-trainable params: 0
_________________________________________________________________

这是我使用的代码：

import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv1D, Flatten
from tensorflow.keras import optimizers
from sklearn.metrics import confusion_matrix
import tensorflow as tf
import tensorflow.keras.metrics

    data = pd.read_csv('Step1_reducedfile.csv',skiprows = 1,header = None)
    data = data.sample(frac=1).reset_index(drop=True)
    train_X = data[0:data.shape[0],0:566]
    train_y = data[0:data.shape[0],566:data.shape[1]]
    train_X = train_X.reshape((train_X.shape[0], train_X.shape[1], 1))
    
    import random
    
    neurons = 50
    
            
    strategy = tensorflow.distribute.MirroredStrategy()             
    with strategy.scope():
        model = tf.keras.Sequential([
          tf.keras.layers.Conv1D(64,kernel_size = 3,activation='relu',input_shape=train_X.shape[1:]),
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(10, activation='softmax'),])
        model.summary()
        sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.24, nesterov=True)
        model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy',tensorflow.keras.metrics.Precision()])
        model.summary()
        results = model.fit(train_X,train_y,validation_split = 0.2,epochs=10,batch_size = 100)
        print(results)

我收到以下错误：

ValueError：在用户代码中：

/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
    return step_function(self, iterator)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_strategy.py:585 _call_for_each_replica
    self._container_strategy(), fn, args, kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_run.py:96 call_for_each_replica
    return _call_for_each_replica(strategy, fn, args, kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_run.py:237 _call_for_each_replica
    coord.join(threads)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/training/coordinator.py:389 join
    six.reraise(*self._exc_info_to_raise)
/usr/local/lib/python3.6/site-packages/six.py:703 reraise
    raise value
/usr/local/lib64/python3.6/site-packages/tensorflow/python/training/coordinator.py:297 stop_on_exception
    yield
/usr/local/lib64/python3.6/site-packages/tensorflow/python/distribute/mirrored_run.py:323 run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
    outputs = model.train_step(data)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/training.py:749 train_step
    y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/engine/compile_utils.py:204 __call__
    loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:149 __call__
    losses = ag_call(y_true, y_pred)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:253 call  **
    return ag_fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
    return target(*args, **kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/losses.py:1535 categorical_crossentropy
    return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
    return target(*args, **kwargs)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/keras/backend.py:4687 categorical_crossentropy
    target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib64/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py:1134 assert_is_compatible_with
    raise ValueError("Shapes %s and %s are incompatible" % (self, other))

ValueError: Shapes (None, 9) and (None, 10) are incompatible

Answer 1

该错误表明您为 model 提供了错误形状的 label 阵列。 它期待一个形状数组 (None, 9)，而您正在给出一个形状数组 (None, 10)。 这可能是因为您的数据集有9个类，正如 Dr.Snoopy 正确提到的那样。

为了社区的利益，我在这里提供完整的工作代码。

import pandas as pd
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv1D, Flatten
from tensorflow.keras import optimizers
from sklearn.metrics import confusion_matrix
import tensorflow as tf
import tensorflow.keras.metrics

    data = pd.read_csv('Step1_reducedfile.csv',skiprows = 1,header = None)
    data = data.sample(frac=1).reset_index(drop=True)
    train_X = data[0:data.shape[0],0:566]
    train_y = data[0:data.shape[0],566:data.shape[1]]
    train_X = train_X.reshape((train_X.shape[0], train_X.shape[1], 1))
    
    import random
    
    neurons = 50
    
            
    strategy = tensorflow.distribute.MirroredStrategy()             
    with strategy.scope():
        model = tf.keras.Sequential([
          tf.keras.layers.Conv1D(64,kernel_size = 3,activation='relu',input_shape=train_X.shape[1:]),
          tf.keras.layers.Flatten(),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(neurons,activation='relu'),
          tf.keras.layers.Dense(9, activation='softmax'),])
        model.summary()
        sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.24, nesterov=True)
        model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy',tensorflow.keras.metrics.Precision()])
        model.summary()
        results = model.fit(train_X,train_y,validation_split = 0.2,epochs=10,batch_size = 100)
        print(results)