Tensorflow keras Conv1d input_shape problem?

Question

i have function that creates a ann model

def create_model(input_dim, n_action):
""" A multi-layer perceptron """

model = tf.keras.Sequential()

model.add(tf.keras.layers.Conv1D(
    128,
    kernel_size=5,
    padding='same',
    activation=tf.keras.activations.relu,
    input_shape=(101,)
))

model.add(tf.keras.layers.Conv1D(
    64,
    kernel_size=5,
    padding='same',
    activation=tf.keras.activations.relu,
))

model.add(tf.keras.layers.Dense(
    64,
    activation=tf.keras.activations.relu,
))

model.add(
    tf.keras.layers.Dense(
        64,
        activation=tf.keras.activations.relu,
    ))

model.add(
    tf.keras.layers.Dense(
        32,
        activation=tf.keras.activations.relu,
    ))

model.add(
    tf.keras.layers.Dense(
        n_action,
        activation=tf.keras.activations.relu,
    ))

model.compile(loss='mse', optimizer='adam')

print((model.summary()))

return model

as you can see the first layer is a 1d convolutional layer with filters 128 and kelrnel size 5.

my dataset is just an array of numbers of length 101

[584.95 582.3  581.7  ... 392.35 391.8  391.3 ]

but when i do

model.train_on_batch(states, target_full)

where states are arrays of length 101 and batch size 32 i get this error.

Input 0 of layer "conv1d_118" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 101)

I don't understand why dose a 1dconv need 3 dimensional input.Can anyone help me solve this error?

Any help is very much appreciated.