Models: ValueError: The first layer in a Sequential model must get an `input_shape` or `batch_input_shape` argument.

[3dstorm]

import tensorflow as tf

mnist = tf.keras.datasets.mnist # 28x28 images of handwritten digits 0-9

(x_train, y_train),(x_test, y_test) = mnist.load_data()

x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))

model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

model.fit(x_train, y_train, epochs=3)

ValueError Traceback (most recent call last)
in ()
9
10 model = tf.keras.models.Sequential()
---> 11 model.add(tf.keras.layers.Flatten())
12 model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
13 model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))

E:\Anaconda3\lib\site-packages\tensorflow\python\keras_impl\keras\models.py in add(self, layer)
459 # create an input layer
460 if not hasattr(layer, '_batch_input_shape'):
--> 461 raise ValueError('The first layer in a '
462 'Sequential model must '
463 'get an input_shape or '

ValueError: The first layer in a Sequential model must get an input_shape or batch_input_shape argument.

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[ahmernajar]

You should use flatten() at the end of your layers

[ahmernajar]

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))

model.add(tf.keras.layers.Flatten())