我是一个TensorFlow的新手。我已经按照一些教程学习，并完成了我的第一个多类分类模型。
我不确定我的层设计是否合理，但无论如何，测试集上的准确率大约是0.98。
问题是我无法用我的模型预测新的输入。以下是我使用的代码和训练模型的数据。
数据有10列，最后一列是类别名称。模型的目的是使用一行9个值来预测该行属于哪个类别。
所有代码都在colab中运行的。
!pip install sklearnimport pandas as pdimport numpy as npimport tensorflow as tffrom tensorflow import feature_columnfrom tensorflow.keras import layersfrom tensorflow.keras import Sequentialfrom sklearn.model_selection import train_test_splitindex_col = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'r']dataframe = pd.read_csv('drive/MyDrive/Book2.csv', names=index_col)train, test = train_test_split(dataframe, test_size=0.2)train, val = train_test_split(train, test_size=0.2)train_labels = train.filter('r')train = train.drop('r', axis=1)test_labels = test.filter('r')test = test.drop('r', axis=1)model = tf.keras.Sequential([ tf.keras.layers.Dense(1), tf.keras.layers.Dense(100, activation='relu'), tf.keras.layers.Dense(4)])model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])model.fit(train, train_labels, epochs=20)test_loss, test_acc = model.evaluate(test, test_labels, verbose=2)result = model.predict(pd.DataFrame([1, 3, 0, 3, 3, 1, 2, 3, 2]))这是我得到的控制台错误信息。
---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-29-942b3f127f67> in <module>()----> 1 result = model.predict([1, 3, 0, 3, 3, 1, 2, 3, 2])9 frames/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) 984 except Exception as e: # pylint:disable=broad-except 985 if hasattr(e, "ag_error_metadata"):--> 986 raise e.ag_error_metadata.to_exception(e) 987 else: 988 raiseValueError: in user code: /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1569 predict_function * return step_function(self, iterator) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1559 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1285 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica return fn(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1552 run_step ** outputs = model.predict_step(data) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1525 predict_step return self(x, training=False) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1013 __call__ input_spec.assert_input_compatibility(self.input_spec, inputs, self.name) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py:255 assert_input_compatibility ' but received input with shape ' + display_shape(x.shape)) ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected axis -1 of input shape to have value 9 but received input with shape (None, 1)Book2.csv文件在这里。
回答：
你传递给predict的dataframe形状为(9,1)。它的形状应该像你传递的训练数据集的形状（除了第一维度）。
简单地转置你的数据，将形状从(9,1)更改为(1,9)：
result = model.predict(pd.DataFrame([1, 3, 0, 3, 3, 1, 2, 3, 2]).T)补充说明：(9,1)表示9个样本，每个样本有1个特征，这与你的模型期望不符。但(1,9)表示1个样本有9个特征。
