I’m confused about what dimensions input shapes in keras models should be.
From the course, it seems that the input shape should be the same dimensions of a single record in the data set.
Example from week 3, course 1 lab (C1_W3_Lab_1_improving_accuracy_using_convolutions):
model = tf.keras.models.Sequential([
-
tf.keras.layers.Conv2D(64, (3,3), activation=‘relu’, input_shape=(28, 28, 1)),*
-
tf.keras.layers.MaxPooling2D(2, 2),*
etc…
However, elsewhere, I have read that the input shape should be same dimensions as the training set. For example, see the following S.O. post:
Blockquote
In Keras, the input layer itself is not a layer, but a tensor. It’s the starting tensor you send to the first hidden layer. This tensor must have the same shape as your training data . Example: if you have 30 images of 50x50 pixels in RGB (3 channels), the shape of your input data is (30,50,50,3)
neural network - Keras input explanation: input_shape, units, batch_size, dim, etc - Stack Overflow)%20
The above contradicts the input shape of the model in the lab, right? Or can you do either?
Or, am i missing something?
thanks