ValueError: Input 0 of layer sequential_22 is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [28, 28, 1] - tensorflow

Question

from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
from keras.datasets import mnist
from keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
I am getting an error:
ValueError: Input 0 of layer sequential_22 is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [28, 28, 1]

Answer 1

I tried using the Keras 2.8, fashion_mnist dataset and it is working.
import keras
print(keras.__version__)
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
from keras.datasets import fashion_mnist
from tensorflow.keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
Output:
2.8.0
Epoch 1/5
938/938 [==============================] - 53s 55ms/step - loss: 0.5523 - accuracy: 0.7965
Epoch 2/5
938/938 [==============================] - 51s 55ms/step - loss: 0.3333 - accuracy: 0.8780
Epoch 3/5
938/938 [==============================] - 54s 58ms/step - loss: 0.2832 - accuracy: 0.8968
Epoch 4/5
938/938 [==============================] - 52s 55ms/step - loss: 0.2508 - accuracy: 0.9079
Epoch 5/5
938/938 [==============================] - 51s 55ms/step - loss: 0.2264 - accuracy: 0.9168
<keras.callbacks.History at 0x7f1c1b0a5390>