ظهور الخطأ ValueError: Input 0 of layer sequential_6 is incompatible with the layer في كيراس Keras

قمت ببناء النموذج التالي في كيراس لكن يظهر لي الخطأ التالي: 

from keras.datasets import reuters
import keras
import tensorflow as tf
(train_data, train_labels), (test_data, test_labels) = reuters.load_data(
num_words=10000)
import numpy as np
def vectorize_sequences(sequences, dimension=10000):
  results = np.zeros((len(sequences), dimension))
  for i, sequence in enumerate(sequences):
   results[i, sequence] = 1.
  return results
x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)
from keras.utils.np_utils import to_categorical
one_hot_train_labels = to_categorical(train_labels)
one_hot_test_labels = to_categorical(test_labels)
x_val = x_train[:1000]
partial_x_train = x_train[1000:]
# هنا
y_val = one_hot_train_labels[:1000]
partial_y_train = one_hot_train_labels[1000:]
# الآن:
partial_x_train.shape
# (7982, 10000)
partial_y_train.shape
# (7982, 46)
from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(1000,)))
model.add(keras.layers.LayerNormalization())
model.add(layers.Dense(64, activation='relu'))
model.add(keras.layers.LayerNormalization())
model.add(layers.Dense(46, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['CategoricalAccuracy'])
history = model.fit(tf.convert_to_tensor(partial_x_train, np.float32),
partial_y_train,
epochs=6,
batch_size=512,
validation_data=(x_val, y_val))
----------------------------------------------------------
 ValueError: Input 0 of layer sequential_7 is incompatible with the layer: expected axis -1 of input shape to have value 1000 but received input with shape (None, 10000)