Tensorflow ValueError:形状（无，7）和（无，8）不兼容

我不断得到相同的错误，我想我有输入形状的问题，请帮助我

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

因为这是一个多类别的分类问题

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]

模型

 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])

我在尝试拟合模型时遇到错误。

这是正在运行的代码。 由于您没有提供任何样本数据，我不得不伪造一些数据，我将解释问题所在。 由于softmax层为8，因此您的y_列深度必须为8。 如果您想在我的代码中得到相同的错误，请更改

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

---

发布代码，而不是代码的屏幕截图我已经按照您的要求发布了快照的代码。打印您的y_火车的形状，您将看到

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]