Out of memory OOM using tensorflow gradient tape but only happens when I append a list

Question

I've been working on a data set (1000,3253) using a CNN. I'm running gradient calculations through gradient tape but it keeps running out of memory. Yet if I remove the line appending a gradient calculation to a list the script runs through all the epochs. I'm not entirely sure why this would happen but I am also new to tensorflow and the use of gradient tape. Any advice or input would be appreciated

        #create a batch loop
    for x, y_true in train_dataset:            
        #create a tape to record actions


        with  tf.GradientTape(watch_accessed_variables=False) as tape:
            x_var = tf.Variable(x)
            tape.watch([model.trainable_variables,x_var])    

            y_pred = model(x_var,training=True)    
            tape.stop_recording()
            loss = los_func(y_true, y_pred)
        epoch_loss_avg.update_state(loss)
        epoch_accuracy.update_state(y_true, y_pred)                

        #pdb.set_trace() 
        gradients,something = tape.gradient(loss, (model.trainable_variables,x_var))
        #sa_input.append(tape.gradient(loss, x_var))
        del tape            


        #apply gradients
        sa_input.append(something)
        opti_func.apply_gradients(zip(gradients, model.trainable_variables)) 
    train_loss_results.append(epoch_loss_avg.result())
    train_accuracy_results.append(epoch_accuracy.result())

Answer 1

As you are new to TF2, would recommend to go through this guide. This guide covers training, evaluation, and prediction (inference) models in TensorFlow 2.0 in two broad situations:

1. When using built-in APIs for training & validation (such as model.fit(), model.evaluate(), model.predict()). This is covered in the section "Using built-in training & evaluation loops".
2. When writing custom loops from scratch using eager execution and the GradientTape object. This is covered in the section "Writing your own training & evaluation loops from scratch".

Below is a program where I am computing the gradients after every epoch and appending to a list. At end of the program I am converting the list to array for simplicity.

Code - This program throws OOM Error error if I use a deep network of many layers and bigger filter size

# Importing dependency
%tensorflow_version 2.x
from tensorflow import keras
from tensorflow.keras import backend as K
from tensorflow.keras import datasets
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
import numpy as np
import tensorflow as tf

# Import Data
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

# Build Model
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32,32, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10))

# Model Summary
model.summary()

# Model Compile 
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Define the Gradient Fucntion
epoch_gradient = []
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)

# Define the Gradient Function
@tf.function
def get_gradient_func(model):
    with tf.GradientTape() as tape:
       logits = model(train_images, training=True)
       loss = loss_fn(train_labels, logits)    
    grad = tape.gradient(loss, model.trainable_weights)
    model.optimizer.apply_gradients(zip(grad, model.trainable_variables))
    return grad

# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    grad = get_gradient_func(model)
    epoch_gradient.append(grad)

epoch = 4

print(train_images.shape, train_labels.shape)

model.fit(train_images, train_labels, epochs=epoch, validation_data=(test_images, test_labels), callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)

Output -

Model: "sequential_5"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_12 (Conv2D)           (None, 30, 30, 32)        896       
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 15, 15, 32)        0         
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 13, 13, 64)        18496     
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 6, 6, 64)          0         
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 4, 4, 64)          36928     
_________________________________________________________________
flatten_4 (Flatten)          (None, 1024)              0         
_________________________________________________________________
dense_11 (Dense)             (None, 64)                65600     
_________________________________________________________________
dense_12 (Dense)             (None, 10)                650       
=================================================================
Total params: 122,570
Trainable params: 122,570
Non-trainable params: 0
_________________________________________________________________
(50000, 32, 32, 3) (50000, 1)
Epoch 1/4
1563/1563 [==============================] - 109s 70ms/step - loss: 1.7026 - accuracy: 0.4081 - val_loss: 1.4490 - val_accuracy: 0.4861
Epoch 2/4
1563/1563 [==============================] - 145s 93ms/step - loss: 1.2657 - accuracy: 0.5506 - val_loss: 1.2076 - val_accuracy: 0.5752
Epoch 3/4
1563/1563 [==============================] - 151s 96ms/step - loss: 1.1103 - accuracy: 0.6097 - val_loss: 1.1122 - val_accuracy: 0.6127
Epoch 4/4
1563/1563 [==============================] - 152s 97ms/step - loss: 1.0075 - accuracy: 0.6475 - val_loss: 1.0508 - val_accuracy: 0.6371
Total number of epochs run: 4

Hope this answers your question. Happy Learning.