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Я продолжаю получать ту же ошибку, я думаю, у меня проблема с входными фигурами, пожалуйста, помогите мне

 X = Features.iloc[: ,:-1].values
Y = Features['labels'].values
 

Поскольку это проблема многоклассовой классификации, необходимо выполнить кодирование нашего Y.

 encoder = OneHotEncoder()
Y = encoder.fit_transform(np.array(Y).reshape(-1,1)).toarray()

# splitting data
x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=0, shuffle=True)
x_train.shape, y_train.shape, x_test.shape, y_test.shape

# scaling our data with sklearn's Standard scaler
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
x_train.shape, y_train.shape, x_test.shape, y_test.shape

# making our data compatible to model.
x_train = np.expand_dims(x_train, axis=2)
x_test = np.expand_dims(x_test, axis=2)
x_train.shape, y_train.shape, x_test.shape, y_test.shape, x_train.shape[1]
 

Модель

  model=Sequential()
    model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu', input_shape=(x_train.shape[1], 1)))
model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(Conv1D(128, kernel_size=5, strides=1, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))
model.add(Dropout(0.2))

model.add(Conv1D(64, kernel_size=5, strides=1, padding='same', activation='relu'))
model.add(MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(Flatten())
model.add(Dense(units=32, activation='relu'))
model.add(Dropout(0.3))

model.add(Dense(units=8, activation='softmax'))
model.compile(optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = ['accuracy'])

model.summary()

rlrp = ReduceLROnPlateau(monitor='val_loss', factor=0.4, verbose=0, patience=2, min_lr=0.0000001)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0)
model.fit(x_train, y_train, batch_size=64, epochs=50, validation_data=(x_test, y_test), callbacks=[rlrp])
#history=model.fit(x_train, y_train, callbacks=[rlrp])
 

Я получаю ошибку при попытке подгонки модели.

Вот ваш запущенный код. Поскольку вы не предоставили никаких выборочных данных, мне пришлось подделать некоторые данные, и я объясню, в чем проблема. Ваш y_train должен иметь глубину 8, поскольку ваш слой softmax равен 8. Если вы хотите получить ту же ошибку в моем коде, измените

 y_train = tf.one_hot(tf.random.uniform(shape=[1000],minval=0, maxval=2, dtype=tf.int32),8)  #change the depth to 7 and you will see your error
 

 import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from matplotlib import pyplot as plt
import numpy as np

x_train = tf.random.normal(shape=(1000,10,1), dtype = tf.float32)
x_test = tf.random.normal(shape=(100,10,1), dtype = tf.float32)
y_train = tf.one_hot(tf.random.uniform(shape=[1000],minval=0, maxval=2, dtype=tf.int32),8)
y_test = tf.one_hot(tf.random.uniform(shape=[100],minval=0, maxval=2, dtype=tf.int32),8)
tf.print(y_train)


model=tf.keras.Sequential()
model.add(layers.Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu', input_shape=(x_train.shape[1], 1)))
model.add(layers.MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(layers.Conv1D(256, kernel_size=5, strides=1, padding='same', activation='relu'))
model.add(layers.MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(layers.Conv1D(128, kernel_size=5, strides=1, padding='same', activation='relu'))
model.add(layers.MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))
model.add(layers.Dropout(0.2))

model.add(layers.Conv1D(64, kernel_size=5, strides=1, padding='same', activation='relu'))
model.add(layers.MaxPooling1D(pool_size=5, strides = 2, padding = 'same'))

model.add(layers.Flatten())
model.add(layers.Dense(units=32, activation='relu'))
model.add(layers.Dropout(0.3))

model.add(layers.Dense(units=8, activation='softmax'))
model.compile(optimizer = 'adam' , loss = 'categorical_crossentropy' , metrics = ['accuracy'])

model.summary()

rlrp = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.4, verbose=0, patience=2, min_lr=0.0000001)
reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0)
model.fit(x_train, y_train, batch_size=64, epochs=50, validation_data=(x_test, y_test), callbacks=[rlrp])
#history=model.fit(x_train, y_train, callbacks=[rlrp])

encoder = OneHotEncoder()
Y = encoder.fit_transform(np.array(Y).reshape(-1,1)).toarray()

# splitting data
x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=0, shuffle=True)
x_train.shape, y_train.shape, x_test.shape, y_test.shape

# scaling our data with sklearn's Standard scaler
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
x_train.shape, y_train.shape, x_test.shape, y_test.shape

# making our data compatible to model.
x_train = np.expand_dims(x_train, axis=2)
x_test = np.expand_dims(x_test, axis=2)
x_train.shape, y_train.shape, x_test.shape, y_test.shape, x_train.shape[1]