I am trying to understand why there is a difference between calculating a dense layer operation directly and using the keras implementation.
Following the documentation (https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) tf.keras.layers.Dense() should implement the operation output = activation(dot(input, kernel) + bias) but result and result1 below are not the same.
tf.random.set_seed(1)
bias = tf.Variable(tf.random.uniform(shape=(5,1)), dtype=tf.float32)
kernel = tf.Variable(tf.random.uniform(shape=(5,10)), dtype=tf.float32)
x = tf.constant(tf.random.uniform(shape=(10,1), dtype=tf.float32))
result = tf.nn.relu(tf.linalg.matmul(a=kernel, b=x) + bias)
tf.print(result)
test = tf.keras.layers.Dense(units = 5,
activation = 'relu',
use_bias = True,
kernel_initializer = tf.keras.initializers.Constant(value=kernel),
bias_initializer = tf.keras.initializers.Constant(value=bias),
dtype=tf.float32)
result1 = test(tf.transpose(x))
print()
tf.print(result1)
output
[[2.87080455]
[3.25458574]
[3.28776264]
[3.14319134]
[2.04760242]]
[[2.38769 3.63470697 2.62423944 3.31286287 2.91121125]]
Using test.get_weights() I can see that the kernel and bias (b) are getting set to the correct values. I am using TF version 2.12.0.
After some experimentation I realized that the kernel for the dense layer needs to be of shape=(10,5) as apposed to (5,10) as in the code from the original question above. This is implicit because units=5 so a vector of size 10 needs to be passed (hence why input_shape=(10,) is commented out as a reminder). Below is the corrected code:
tf.random.set_seed(1)
bias = tf.Variable(tf.random.uniform(shape=(5,1)), dtype=tf.float32)
kernel = tf.Variable(tf.random.uniform(shape=(10,5)), dtype=tf.float32)
x = tf.constant(tf.random.uniform(shape=(10,1), dtype=tf.float32))
result = tf.nn.relu(tf.linalg.matmul(a=kernel, b=x, transpose_a=True) + bias)
tf.print(result)
test = tf.keras.layers.Dense(units = 5,
# input_shape=(10,),
activation = 'relu',
use_bias = True,
kernel_initializer = tf.keras.initializers.Constant(value=kernel),
bias_initializer = tf.keras.initializers.Constant(value=bias),
dtype=tf.float32)
result1 = test(tf.transpose(x))
print()
tf.print(result1)
[[2.38769]
[3.63470697]
[2.62423944]
[3.31286287]
[2.91121125]]
[[2.38769 3.63470697 2.62423944 3.31286287 2.91121125]]
Ultimately, I am not entirely sure what was happening under the hood and why keras did not raise an error. I will check with the tf.keras.layers.Dense() implementation but any thoughts or suggestions by someone who knows the code already are highly appreciated!