Converting a tensor to a a numpy array inside custom training loop in TensorFlow 1.15

I am in the process of training a model in TensorFlow and I want to print the loss of the model after each batch. I am using a custom training loop that looks something like

import tensorflow as tf
from tensorflow.keras.losses import cosine_similarity
from tensorflow.keras.optimizers import Adam


model = get_model(**model_params)
g = get_generator(**generator_params)

optimizer = Adam()
epochs = 10

for epoch in range(epochs):
   for i in range(len(g)):
      with tf.GradientTape() as tape:
         x,y = g[i]
         model_prediction = model(x)
         loss = cosine_similarity(y, model_prediction)
         gradients = tape.gradient(loss, model.trainable_weights)
         optimizer.apply_gradients(zip(gradients, model.trainable_weights))
         
         print(f"Batch {i}/{len(g)}. Loss: {loss.eval(session=tf.Session()): .4f}")

Since the loss is a tensor to be able to actually see the values I need to convert it to a NumPy array (the plan isn't to print the array but once I can convert the tensor into an array, that solves my problem). The way I have been trying it, unfortunately, results in the following error

Failed precondition: Error while reading resource variable dense_5/kernel from Container: localhost. 
This could mean that the variable was uninitialized. Not found: Container localhost does not exist.`

I have also tried editing the loop by adding

for epoch in range(epochs):
   for i in range(len(g)):
      with tf.GradientTape() as tape, tf.Session() as session:
         # training code
         loss_numpy = session.run(loss)

This gives me the same error as above, and also have tried initializing the global variables at each training step

for epoch in range(epochs):
   for i in range(len(g)):
      with tf.GradientTape() as tape, tf.Session() as session:
         # training code
         init = tf.global_variables_initializer()
         session.run(init)
         print(f"Batch {i}/{len(g)}. Loss: {session.run(loss): .4f}")

Which does not throw an error but is pretty slow and outputs a lot of other Nvidia-related stuff that I would like to avoid.

Is there a way to avoid the error but not have to do the variable initialization at each step. Or perhaps there is a way to silence the Nvidia-related output.