problem using convolutional autoencoder for 2d data - tensorflow

Question

I want to train an autoencoder for the purpose of gpr investigations.
The input data dimension is 149x8.However, While i am trying deep autoencoder it works fine
input_img = Input(shape=(8,))
encoded1 = Dense(8, activation='relu')(input_img)
encoded2 = Dense(4, activation='relu')(encoded1)
encoded3 = Dense(2, activation='relu' )(encoded2)
decoded1 = Dense(2, activation='relu' )(encoded3)
decoded2 = Dense(4, activation='relu')(decoded1)
decoded3 = Dense(8, activation='relu' )(decoded2)
decoded = Dense(8, activation='linear')(decoded3)
autoencoder = Model(input_img, decoded)
sgd = optimizers.Adam(lr=0.001)
autoencoder.compile(optimizer=sgd, loss='mse')
autoencoder.summary()
..................................................
But while trying to use convolutional autoencoder for the same input
it gives error `ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=2`
can anybody suggest me how to overcome this problem.
My code is
input_img = Input(shape=(8,))
x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(16, (3, 3), activation='relu')(x)
x = layers.UpSampling2D((2, 2))(x)
decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
sgd = optimizers.Adam(lr=0.001)
autoencoder.compile(optimizer=sgd, loss='mse')
autoencoder.summary()

Answer 1

Wrong Input Shape:
This is because we are passing the input shape of (8,) and 1 extra dimension added by TensorFlow for Batch size, so the error message says that it found ndim=3, but the CNN has expected min_ndim=4, 3 for the image size and 1 for the batch size. e.g.
input_shape=(number_of_rows, 28,28,1)