ValueError: sequential_4层的输入0与层：：预期的min_ndim=3，found ndim=2不兼容。接收到的完整形状：(无，5000)

Question

我正在做一个项目，我需要分离睡眠数据和它的标签。但是我被上面提到的错误困住了。

由于我是机器学习方面的新手，如果有人能帮我解决这个问题，我将非常感激。

我使用以下代码实现了一个模型：

EEG_training_data = EEG_training_data.reshape(EEG_training_data.shape[0],   EEG_training_data.shape[1],1)
print(EEG_training_data.shape)# (5360, 5000, 1)
EEG_validation_data = EEG_validation_data.reshape(EEG_validation_data.shape[0], EEG_validation_data.shape[1],1)
print(EEG_validation_data.shape)#(1396, 5000, 1)

label_class = (np.unique(EEG_training_label))
num_classes = label_class.size # num_classes = 5

#define the model using CNN
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv1D(filters=64, kernel_size= 16, activation='relu', batch_input_shape=(None,5000, 1)))  # #input_shape=(5000, 1)
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.MaxPool1D(8, padding='same'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))

#Summary of the model defined:
model.summary()

#Define loss function
model.compile(
                      loss=  'categorical_crossentropy', # 'sparse_categorical_crossentropy',
                      optimizer='adam',
                      metrics=[tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives(), 'accuracy'])

#one Hot Encoding
y_train_hot = tf.keras.utils.to_categorical(EEG_training_label, num_classes) 
print('New y_train shape: ', y_train_hot.shape)#(5360, 5)

y_valid_hot = tf.keras.utils.to_categorical(EEG_validation_label, num_classes)
print('New y_valid shape: ', y_valid_hot.shape)#(1396, 5)

# apply fit on data
model_history = model.fit(
                            x=EEG_training_data,
                            y=y_train_hot, 
                            batch_size=32,
                            epochs=5,
                            validation_data=(EEG_validation_data, y_valid_hot),
                             )



model_prediction = model.predict(EEG_testing_data)
predicted_matrix = tf.math.confusion_matrix(labels=EEG_testing_label.argmax(axis=1), predictions=model_prediction.argmax(axis=1)).numpy()
print(predicted_matrix)

Answer 1

我没有体验到你所提供的代码的问题。试着执行以下代码，这些代码应该会像预期的那样工作。如果是这样的话，请再次检查所有数据的形状，如EEG_training_data等，如下所示：

import tensorflow as tf
import numpy as np

EEG_training_data = np.ones((5360, 5000, 1))
EEG_validation_data = np.ones((1396, 5000, 1))
EEG_training_label = np.random.randint(5, size=5360)
EEG_validation_label = np.random.randint(5, size=1396)

label_class = (np.unique(EEG_training_label))
num_classes = label_class.size
print(num_classes)  # prints 5

#define the model using CNN
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv1D(filters=64, kernel_size= 16, activation='relu', batch_input_shape=(None, 5000, 1)))  # input_shape=(5000, 1)
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.MaxPool1D(8, padding='same'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))

#Summary of the model defined:
model.summary()

#Define loss function
model.compile(loss = 'categorical_crossentropy',
              optimizer='adam',
              metrics=[tf.keras.metrics.FalseNegatives(), tf.keras.metrics.FalsePositives(), 'accuracy'])

#one Hot Encoding
y_train_hot = tf.keras.utils.to_categorical(EEG_training_label, num_classes) 

print('New y_train shape: ', y_train_hot.shape)  #(5360, 5)

y_valid_hot = tf.keras.utils.to_categorical(EEG_validation_label, num_classes)
print('New y_valid shape: ', y_valid_hot.shape)  #(1396, 5)

# apply fit on data
model_history = model.fit(x=EEG_training_data,
                          y=y_train_hot, 
                          batch_size=32,
                          epochs=5,
                          validation_data=(EEG_validation_data, y_valid_hot),
)

model_prediction = model.predict(EEG_validation_data)
predicted_matrix = tf.math.confusion_matrix(labels=EEG_validation_label.argmax(axis=1), predictions=model_prediction.argmax(axis=1)).numpy()
print(predicted_matrix)