Anna KaRNNa Hyperparameters


Anna KaRNNa

In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.

This network is based off of Andrej Karpathy's post on RNNs and implementation in Torch. Also, some information here at r2rt and from Sherjil Ozair on GitHub. Below is the general architecture of the character-wise RNN.


In [1]:
import time
from collections import namedtuple

import numpy as np
import tensorflow as tf

First we'll load the text file and convert it into integers for our network to use.


In [2]:
with open('anna.txt', 'r') as f:
    text=f.read()
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
chars = np.array([vocab_to_int[c] for c in text], dtype=np.int32)

In [3]:
text[:100]


Out[3]:
'Chapter 1\n\n\nHappy families are all alike; every unhappy family is unhappy in its own\nway.\n\nEverythin'

In [4]:
chars[:100]


Out[4]:
array([42, 74, 81, 14, 15,  4, 75,  5, 29, 51, 51, 51, 19, 81, 14, 14, 63,
        5, 45, 81, 50, 39, 35, 39,  4, 34,  5, 81, 75,  4,  5, 81, 35, 35,
        5, 81, 35, 39, 23,  4, 10,  5,  4, 43,  4, 75, 63,  5, 65, 82, 74,
       81, 14, 14, 63,  5, 45, 81, 50, 39, 35, 63,  5, 39, 34,  5, 65, 82,
       74, 81, 14, 14, 63,  5, 39, 82,  5, 39, 15, 34,  5,  6, 16, 82, 51,
       16, 81, 63, 64, 51, 51, 59, 43,  4, 75, 63, 15, 74, 39, 82], dtype=int32)

Now I need to split up the data into batches, and into training and validation sets. I should be making a test set here, but I'm not going to worry about that. My test will be if the network can generate new text.

Here I'll make both input and target arrays. The targets are the same as the inputs, except shifted one character over. I'll also drop the last bit of data so that I'll only have completely full batches.

The idea here is to make a 2D matrix where the number of rows is equal to the number of batches. Each row will be one long concatenated string from the character data. We'll split this data into a training set and validation set using the split_frac keyword. This will keep 90% of the batches in the training set, the other 10% in the validation set.


In [5]:
def split_data(chars, batch_size, num_steps, split_frac=0.9):
    """ 
    Split character data into training and validation sets, inputs and targets for each set.
    
    Arguments
    ---------
    chars: character array
    batch_size: Size of examples in each of batch
    num_steps: Number of sequence steps to keep in the input and pass to the network
    split_frac: Fraction of batches to keep in the training set
    
    
    Returns train_x, train_y, val_x, val_y
    """
    
    slice_size = batch_size * num_steps
    n_batches = int(len(chars) / slice_size)
    
    # Drop the last few characters to make only full batches
    x = chars[: n_batches*slice_size]
    y = chars[1: n_batches*slice_size + 1]
    
    # Split the data into batch_size slices, then stack them into a 2D matrix 
    x = np.stack(np.split(x, batch_size))
    y = np.stack(np.split(y, batch_size))
    
    # Now x and y are arrays with dimensions batch_size x n_batches*num_steps
    
    # Split into training and validation sets, keep the virst split_frac batches for training
    split_idx = int(n_batches*split_frac)
    train_x, train_y= x[:, :split_idx*num_steps], y[:, :split_idx*num_steps]
    val_x, val_y = x[:, split_idx*num_steps:], y[:, split_idx*num_steps:]
    
    return train_x, train_y, val_x, val_y

In [6]:
train_x, train_y, val_x, val_y = split_data(chars, 10, 200)

In [7]:
train_x.shape


Out[7]:
(10, 178400)

In [8]:
train_x[:,:10]


Out[8]:
array([[42, 74, 81, 14, 15,  4, 75,  5, 29, 51],
       [12, 82, 47,  5, 74,  4,  5, 50,  6, 43],
       [ 5, 18, 81, 15, 18, 74, 39, 82, 53,  5],
       [ 6, 15, 74,  4, 75,  5, 16,  6, 65, 35],
       [ 5, 15, 74,  4,  5, 35, 81, 82, 47, 76],
       [ 5, 70, 74, 75,  6, 65, 53, 74,  5, 35],
       [15,  5, 15,  6, 51, 47,  6, 64, 51, 51],
       [ 6,  5, 74,  4, 75, 34,  4, 35, 45, 30],
       [74, 81, 15,  5, 39, 34,  5, 15, 74,  4],
       [ 4, 75, 34,  4, 35, 45,  5, 81, 82, 47]], dtype=int32)

I'll write another function to grab batches out of the arrays made by split data. Here each batch will be a sliding window on these arrays with size batch_size X num_steps. For example, if we want our network to train on a sequence of 100 characters, num_steps = 100. For the next batch, we'll shift this window the next sequence of num_steps characters. In this way we can feed batches to the network and the cell states will continue through on each batch.


In [9]:
def get_batch(arrs, num_steps):
    batch_size, slice_size = arrs[0].shape
    
    n_batches = int(slice_size/num_steps)
    for b in range(n_batches):
        yield [x[:, b*num_steps: (b+1)*num_steps] for x in arrs]

In [10]:
def build_rnn(num_classes, batch_size=50, num_steps=50, lstm_size=128, num_layers=2,
              learning_rate=0.001, grad_clip=5, sampling=False):
        
    if sampling == True:
        batch_size, num_steps = 1, 1

    tf.reset_default_graph()
    
    # Declare placeholders we'll feed into the graph
    with tf.name_scope('inputs'):
        inputs = tf.placeholder(tf.int32, [batch_size, num_steps], name='inputs')
        x_one_hot = tf.one_hot(inputs, num_classes, name='x_one_hot')
    
    with tf.name_scope('targets'):
        targets = tf.placeholder(tf.int32, [batch_size, num_steps], name='targets')
        y_one_hot = tf.one_hot(targets, num_classes, name='y_one_hot')
        y_reshaped = tf.reshape(y_one_hot, [-1, num_classes])
    
    keep_prob = tf.placeholder(tf.float32, name='keep_prob')
    
    # Build the RNN layers
    with tf.name_scope("RNN_cells"):
        lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
        drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
        cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)
    
    with tf.name_scope("RNN_init_state"):
        initial_state = cell.zero_state(batch_size, tf.float32)

    # Run the data through the RNN layers
    with tf.name_scope("RNN_forward"):
        rnn_inputs = [tf.squeeze(i, squeeze_dims=[1]) for i in tf.split(x_one_hot, num_steps, 1)]
        outputs, state = tf.contrib.rnn.static_rnn(cell, rnn_inputs, initial_state=initial_state)
    
    final_state = state
    
    # Reshape output so it's a bunch of rows, one row for each cell output
    with tf.name_scope('sequence_reshape'):
        seq_output = tf.concat(outputs, axis=1,name='seq_output')
        output = tf.reshape(seq_output, [-1, lstm_size], name='graph_output')
    
    # Now connect the RNN outputs to a softmax layer and calculate the cost
    with tf.name_scope('logits'):
        softmax_w = tf.Variable(tf.truncated_normal((lstm_size, num_classes), stddev=0.1),
                               name='softmax_w')
        softmax_b = tf.Variable(tf.zeros(num_classes), name='softmax_b')
        logits = tf.matmul(output, softmax_w) + softmax_b
        tf.summary.histogram('softmax_w', softmax_w)
        tf.summary.histogram('softmax_b', softmax_b)

    with tf.name_scope('predictions'):
        preds = tf.nn.softmax(logits, name='predictions')
        tf.summary.histogram('predictions', preds)
    
    with tf.name_scope('cost'):
        loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped, name='loss')
        cost = tf.reduce_mean(loss, name='cost')
        tf.summary.scalar('cost', cost)

    # Optimizer for training, using gradient clipping to control exploding gradients
    with tf.name_scope('train'):
        tvars = tf.trainable_variables()
        grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars), grad_clip)
        train_op = tf.train.AdamOptimizer(learning_rate)
        optimizer = train_op.apply_gradients(zip(grads, tvars))
    
    merged = tf.summary.merge_all()
    
    # Export the nodes 
    export_nodes = ['inputs', 'targets', 'initial_state', 'final_state',
                    'keep_prob', 'cost', 'preds', 'optimizer', 'merged']
    Graph = namedtuple('Graph', export_nodes)
    local_dict = locals()
    graph = Graph(*[local_dict[each] for each in export_nodes])
    
    return graph

Hyperparameters

Here I'm defining the hyperparameters for the network. The two you probably haven't seen before are lstm_size and num_layers. These set the number of hidden units in the LSTM layers and the number of LSTM layers, respectively. Of course, making these bigger will improve the network's performance but you'll have to watch out for overfitting. If your validation loss is much larger than the training loss, you're probably overfitting. Decrease the size of the network or decrease the dropout keep probability.


In [15]:
batch_size = 100
num_steps = 100
lstm_size = 512
num_layers = 2
learning_rate = 0.001

Training

Time for training which is is pretty straightforward. Here I pass in some data, and get an LSTM state back. Then I pass that state back in to the network so the next batch can continue the state from the previous batch. And every so often (set by save_every_n) I calculate the validation loss and save a checkpoint.


In [13]:
!mkdir -p checkpoints/anna

In [13]:
def train(model, epochs, file_writer):
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        # Use the line below to load a checkpoint and resume training
        #saver.restore(sess, 'checkpoints/anna20.ckpt')

        n_batches = int(train_x.shape[1]/num_steps)
        iterations = n_batches * epochs
        for e in range(epochs):

            # Train network
            new_state = sess.run(model.initial_state)
            loss = 0
            for b, (x, y) in enumerate(get_batch([train_x, train_y], num_steps), 1):
                iteration = e*n_batches + b
                start = time.time()
                feed = {model.inputs: x,
                        model.targets: y,
                        model.keep_prob: 0.5,
                        model.initial_state: new_state}
                summary, batch_loss, new_state, _ = sess.run([model.merged, model.cost, 
                                                              model.final_state, model.optimizer], 
                                                              feed_dict=feed)
                loss += batch_loss
                end = time.time()
                print('Epoch {}/{} '.format(e+1, epochs),
                      'Iteration {}/{}'.format(iteration, iterations),
                      'Training loss: {:.4f}'.format(loss/b),
                      '{:.4f} sec/batch'.format((end-start)))

                file_writer.add_summary(summary, iteration)

In [15]:
epochs = 20
batch_size = 100
num_steps = 100
train_x, train_y, val_x, val_y = split_data(chars, batch_size, num_steps)

for lstm_size in [128,256,512]:
    for num_layers in [1, 2]:
        for learning_rate in [0.002, 0.001]:
            log_string = 'logs/4/lr={},rl={},ru={}'.format(learning_rate, num_layers, lstm_size)
            writer = tf.summary.FileWriter(log_string)
            model = build_rnn(len(vocab), 
                    batch_size=batch_size,
                    num_steps=num_steps,
                    learning_rate=learning_rate,
                    lstm_size=lstm_size,
                    num_layers=num_layers)
            
            train(model, epochs, writer)


Epoch 1/20  Iteration 1/3560 Training loss: 4.4166 0.6227 sec/batch
Epoch 1/20  Iteration 2/3560 Training loss: 4.4060 0.0297 sec/batch
Epoch 1/20  Iteration 3/3560 Training loss: 4.3931 0.0292 sec/batch
Epoch 1/20  Iteration 4/3560 Training loss: 4.3737 0.0312 sec/batch
Epoch 1/20  Iteration 5/3560 Training loss: 4.3372 0.0347 sec/batch
Epoch 1/20  Iteration 6/3560 Training loss: 4.2606 0.0412 sec/batch
Epoch 1/20  Iteration 7/3560 Training loss: 4.1762 0.0489 sec/batch
Epoch 1/20  Iteration 8/3560 Training loss: 4.1022 0.0450 sec/batch
Epoch 1/20  Iteration 9/3560 Training loss: 4.0332 0.0439 sec/batch
Epoch 1/20  Iteration 10/3560 Training loss: 3.9722 0.0439 sec/batch
Epoch 1/20  Iteration 11/3560 Training loss: 3.9176 0.0445 sec/batch
Epoch 1/20  Iteration 12/3560 Training loss: 3.8715 0.0548 sec/batch
Epoch 1/20  Iteration 13/3560 Training loss: 3.8303 0.0448 sec/batch
Epoch 1/20  Iteration 14/3560 Training loss: 3.7942 0.0443 sec/batch
Epoch 1/20  Iteration 15/3560 Training loss: 3.7616 0.0448 sec/batch
Epoch 1/20  Iteration 16/3560 Training loss: 3.7324 0.0459 sec/batch
Epoch 1/20  Iteration 17/3560 Training loss: 3.7053 0.0483 sec/batch
Epoch 1/20  Iteration 18/3560 Training loss: 3.6826 0.0521 sec/batch
Epoch 1/20  Iteration 19/3560 Training loss: 3.6605 0.0460 sec/batch
Epoch 1/20  Iteration 20/3560 Training loss: 3.6386 0.0452 sec/batch
Epoch 1/20  Iteration 21/3560 Training loss: 3.6194 0.0454 sec/batch
Epoch 1/20  Iteration 22/3560 Training loss: 3.6012 0.0465 sec/batch
Epoch 1/20  Iteration 23/3560 Training loss: 3.5842 0.0462 sec/batch
Epoch 1/20  Iteration 24/3560 Training loss: 3.5683 0.0446 sec/batch
Epoch 1/20  Iteration 25/3560 Training loss: 3.5534 0.0446 sec/batch
Epoch 1/20  Iteration 26/3560 Training loss: 3.5402 0.0444 sec/batch
Epoch 1/20  Iteration 27/3560 Training loss: 3.5276 0.0527 sec/batch
Epoch 1/20  Iteration 28/3560 Training loss: 3.5150 0.0445 sec/batch
Epoch 1/20  Iteration 29/3560 Training loss: 3.5035 0.0452 sec/batch
Epoch 1/20  Iteration 30/3560 Training loss: 3.4925 0.0444 sec/batch
Epoch 1/20  Iteration 31/3560 Training loss: 3.4830 0.0466 sec/batch
Epoch 1/20  Iteration 32/3560 Training loss: 3.4730 0.0498 sec/batch
Epoch 1/20  Iteration 33/3560 Training loss: 3.4632 0.0469 sec/batch
Epoch 1/20  Iteration 34/3560 Training loss: 3.4546 0.0458 sec/batch
Epoch 1/20  Iteration 35/3560 Training loss: 3.4457 0.0497 sec/batch
Epoch 1/20  Iteration 36/3560 Training loss: 3.4379 0.0448 sec/batch
Epoch 1/20  Iteration 37/3560 Training loss: 3.4300 0.0476 sec/batch
Epoch 1/20  Iteration 38/3560 Training loss: 3.4222 0.0437 sec/batch
Epoch 1/20  Iteration 39/3560 Training loss: 3.4146 0.0444 sec/batch
Epoch 1/20  Iteration 40/3560 Training loss: 3.4074 0.0445 sec/batch
Epoch 1/20  Iteration 41/3560 Training loss: 3.4007 0.0448 sec/batch
Epoch 1/20  Iteration 42/3560 Training loss: 3.3942 0.0481 sec/batch
Epoch 1/20  Iteration 43/3560 Training loss: 3.3879 0.0445 sec/batch
Epoch 1/20  Iteration 44/3560 Training loss: 3.3818 0.0458 sec/batch
Epoch 1/20  Iteration 45/3560 Training loss: 3.3758 0.0442 sec/batch
Epoch 1/20  Iteration 46/3560 Training loss: 3.3703 0.0444 sec/batch
Epoch 1/20  Iteration 47/3560 Training loss: 3.3651 0.0493 sec/batch
Epoch 1/20  Iteration 48/3560 Training loss: 3.3604 0.0453 sec/batch
Epoch 1/20  Iteration 49/3560 Training loss: 3.3556 0.0451 sec/batch
Epoch 1/20  Iteration 50/3560 Training loss: 3.3510 0.0456 sec/batch
Epoch 1/20  Iteration 51/3560 Training loss: 3.3465 0.0447 sec/batch
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Epoch 1/20  Iteration 53/3560 Training loss: 3.3375 0.0506 sec/batch
Epoch 1/20  Iteration 54/3560 Training loss: 3.3331 0.0468 sec/batch
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Epoch 1/20  Iteration 57/3560 Training loss: 3.3208 0.0482 sec/batch
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Epoch 1/20  Iteration 59/3560 Training loss: 3.3131 0.0447 sec/batch
Epoch 1/20  Iteration 60/3560 Training loss: 3.3095 0.0476 sec/batch
Epoch 1/20  Iteration 61/3560 Training loss: 3.3059 0.0454 sec/batch
Epoch 1/20  Iteration 62/3560 Training loss: 3.3028 0.0467 sec/batch
Epoch 1/20  Iteration 63/3560 Training loss: 3.2998 0.0455 sec/batch
Epoch 1/20  Iteration 64/3560 Training loss: 3.2962 0.0443 sec/batch
Epoch 1/20  Iteration 65/3560 Training loss: 3.2927 0.0489 sec/batch
Epoch 1/20  Iteration 66/3560 Training loss: 3.2897 0.0447 sec/batch
Epoch 1/20  Iteration 67/3560 Training loss: 3.2866 0.0492 sec/batch
Epoch 1/20  Iteration 68/3560 Training loss: 3.2829 0.0446 sec/batch
Epoch 1/20  Iteration 69/3560 Training loss: 3.2796 0.0448 sec/batch
Epoch 1/20  Iteration 70/3560 Training loss: 3.2767 0.0453 sec/batch
Epoch 1/20  Iteration 71/3560 Training loss: 3.2737 0.0449 sec/batch
Epoch 1/20  Iteration 72/3560 Training loss: 3.2710 0.0451 sec/batch
Epoch 1/20  Iteration 73/3560 Training loss: 3.2681 0.0450 sec/batch
Epoch 1/20  Iteration 74/3560 Training loss: 3.2652 0.0452 sec/batch
Epoch 1/20  Iteration 75/3560 Training loss: 3.2625 0.0451 sec/batch
Epoch 1/20  Iteration 76/3560 Training loss: 3.2599 0.0487 sec/batch
Epoch 1/20  Iteration 77/3560 Training loss: 3.2572 0.0491 sec/batch
Epoch 1/20  Iteration 78/3560 Training loss: 3.2545 0.0457 sec/batch
Epoch 1/20  Iteration 79/3560 Training loss: 3.2518 0.0452 sec/batch
Epoch 1/20  Iteration 80/3560 Training loss: 3.2489 0.0445 sec/batch
Epoch 1/20  Iteration 81/3560 Training loss: 3.2462 0.0516 sec/batch
Epoch 1/20  Iteration 82/3560 Training loss: 3.2436 0.0503 sec/batch
Epoch 1/20  Iteration 83/3560 Training loss: 3.2410 0.0450 sec/batch
Epoch 1/20  Iteration 84/3560 Training loss: 3.2384 0.0454 sec/batch
Epoch 1/20  Iteration 85/3560 Training loss: 3.2355 0.0455 sec/batch
Epoch 1/20  Iteration 86/3560 Training loss: 3.2328 0.0453 sec/batch
Epoch 1/20  Iteration 87/3560 Training loss: 3.2302 0.0449 sec/batch
Epoch 1/20  Iteration 88/3560 Training loss: 3.2274 0.0463 sec/batch
Epoch 1/20  Iteration 89/3560 Training loss: 3.2250 0.0511 sec/batch
Epoch 1/20  Iteration 90/3560 Training loss: 3.2224 0.0460 sec/batch
Epoch 1/20  Iteration 91/3560 Training loss: 3.2199 0.0454 sec/batch
Epoch 1/20  Iteration 92/3560 Training loss: 3.2172 0.0467 sec/batch
Epoch 1/20  Iteration 93/3560 Training loss: 3.2146 0.0452 sec/batch
Epoch 1/20  Iteration 94/3560 Training loss: 3.2119 0.0458 sec/batch
Epoch 1/20  Iteration 95/3560 Training loss: 3.2092 0.0472 sec/batch
Epoch 1/20  Iteration 96/3560 Training loss: 3.2064 0.0470 sec/batch
Epoch 1/20  Iteration 97/3560 Training loss: 3.2039 0.0471 sec/batch
Epoch 1/20  Iteration 98/3560 Training loss: 3.2011 0.0513 sec/batch
Epoch 1/20  Iteration 99/3560 Training loss: 3.1984 0.0540 sec/batch
Epoch 1/20  Iteration 100/3560 Training loss: 3.1957 0.0468 sec/batch
Epoch 1/20  Iteration 101/3560 Training loss: 3.1930 0.0456 sec/batch
Epoch 1/20  Iteration 102/3560 Training loss: 3.1903 0.0506 sec/batch
Epoch 1/20  Iteration 103/3560 Training loss: 3.1876 0.0459 sec/batch
Epoch 1/20  Iteration 104/3560 Training loss: 3.1848 0.0458 sec/batch
Epoch 1/20  Iteration 105/3560 Training loss: 3.1820 0.0465 sec/batch
Epoch 1/20  Iteration 106/3560 Training loss: 3.1792 0.0522 sec/batch
Epoch 1/20  Iteration 107/3560 Training loss: 3.1762 0.0506 sec/batch
Epoch 1/20  Iteration 108/3560 Training loss: 3.1734 0.0461 sec/batch
Epoch 1/20  Iteration 109/3560 Training loss: 3.1706 0.0467 sec/batch
Epoch 1/20  Iteration 110/3560 Training loss: 3.1676 0.0462 sec/batch
Epoch 1/20  Iteration 111/3560 Training loss: 3.1647 0.0538 sec/batch
Epoch 1/20  Iteration 112/3560 Training loss: 3.1619 0.0498 sec/batch
Epoch 1/20  Iteration 113/3560 Training loss: 3.1589 0.0470 sec/batch
Epoch 1/20  Iteration 114/3560 Training loss: 3.1559 0.0526 sec/batch
Epoch 1/20  Iteration 115/3560 Training loss: 3.1529 0.0532 sec/batch
Epoch 1/20  Iteration 116/3560 Training loss: 3.1498 0.0468 sec/batch
Epoch 1/20  Iteration 117/3560 Training loss: 3.1469 0.0465 sec/batch
Epoch 1/20  Iteration 118/3560 Training loss: 3.1441 0.0515 sec/batch
Epoch 1/20  Iteration 119/3560 Training loss: 3.1413 0.0467 sec/batch
Epoch 1/20  Iteration 120/3560 Training loss: 3.1384 0.0567 sec/batch
Epoch 1/20  Iteration 121/3560 Training loss: 3.1357 0.0461 sec/batch
Epoch 1/20  Iteration 122/3560 Training loss: 3.1328 0.0525 sec/batch
Epoch 1/20  Iteration 123/3560 Training loss: 3.1299 0.0462 sec/batch
Epoch 1/20  Iteration 124/3560 Training loss: 3.1271 0.0462 sec/batch
Epoch 1/20  Iteration 125/3560 Training loss: 3.1242 0.0560 sec/batch
Epoch 1/20  Iteration 126/3560 Training loss: 3.1211 0.0467 sec/batch
Epoch 1/20  Iteration 127/3560 Training loss: 3.1183 0.0471 sec/batch
Epoch 1/20  Iteration 128/3560 Training loss: 3.1155 0.0464 sec/batch
Epoch 1/20  Iteration 129/3560 Training loss: 3.1125 0.0471 sec/batch
Epoch 1/20  Iteration 130/3560 Training loss: 3.1096 0.0484 sec/batch
Epoch 1/20  Iteration 131/3560 Training loss: 3.1068 0.0465 sec/batch
Epoch 1/20  Iteration 132/3560 Training loss: 3.1038 0.0524 sec/batch
Epoch 1/20  Iteration 133/3560 Training loss: 3.1009 0.0471 sec/batch
Epoch 1/20  Iteration 134/3560 Training loss: 3.0980 0.0551 sec/batch
Epoch 1/20  Iteration 135/3560 Training loss: 3.0949 0.0480 sec/batch
Epoch 1/20  Iteration 136/3560 Training loss: 3.0919 0.0469 sec/batch
Epoch 1/20  Iteration 137/3560 Training loss: 3.0889 0.0465 sec/batch
Epoch 1/20  Iteration 138/3560 Training loss: 3.0859 0.0469 sec/batch
Epoch 1/20  Iteration 139/3560 Training loss: 3.0831 0.0548 sec/batch
Epoch 1/20  Iteration 140/3560 Training loss: 3.0802 0.0471 sec/batch
Epoch 1/20  Iteration 141/3560 Training loss: 3.0774 0.0469 sec/batch
Epoch 1/20  Iteration 142/3560 Training loss: 3.0744 0.0465 sec/batch
Epoch 1/20  Iteration 143/3560 Training loss: 3.0715 0.0523 sec/batch
Epoch 1/20  Iteration 144/3560 Training loss: 3.0686 0.0470 sec/batch
Epoch 1/20  Iteration 145/3560 Training loss: 3.0658 0.0515 sec/batch
Epoch 1/20  Iteration 146/3560 Training loss: 3.0630 0.0479 sec/batch
Epoch 1/20  Iteration 147/3560 Training loss: 3.0602 0.0616 sec/batch
Epoch 1/20  Iteration 148/3560 Training loss: 3.0576 0.0510 sec/batch
Epoch 1/20  Iteration 149/3560 Training loss: 3.0547 0.0527 sec/batch
Epoch 1/20  Iteration 150/3560 Training loss: 3.0518 0.0471 sec/batch
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Epoch 18/20  Iteration 3032/3560 Training loss: 1.7020 0.0667 sec/batch
Epoch 18/20  Iteration 3033/3560 Training loss: 1.7033 0.0769 sec/batch
Epoch 18/20  Iteration 3034/3560 Training loss: 1.7024 0.0686 sec/batch
Epoch 18/20  Iteration 3035/3560 Training loss: 1.7034 0.0660 sec/batch
Epoch 18/20  Iteration 3036/3560 Training loss: 1.7044 0.0644 sec/batch
Epoch 18/20  Iteration 3037/3560 Training loss: 1.7011 0.0654 sec/batch
Epoch 18/20  Iteration 3038/3560 Training loss: 1.7004 0.0641 sec/batch
Epoch 18/20  Iteration 3039/3560 Training loss: 1.7001 0.0677 sec/batch
Epoch 18/20  Iteration 3040/3560 Training loss: 1.7023 0.0650 sec/batch
Epoch 18/20  Iteration 3041/3560 Training loss: 1.7022 0.0671 sec/batch
Epoch 18/20  Iteration 3042/3560 Training loss: 1.7001 0.0662 sec/batch
Epoch 18/20  Iteration 3043/3560 Training loss: 1.7003 0.0663 sec/batch
Epoch 18/20  Iteration 3044/3560 Training loss: 1.7020 0.0712 sec/batch
Epoch 18/20  Iteration 3045/3560 Training loss: 1.7023 0.0655 sec/batch
Epoch 18/20  Iteration 3046/3560 Training loss: 1.7025 0.0746 sec/batch
Epoch 18/20  Iteration 3047/3560 Training loss: 1.7016 0.0707 sec/batch
Epoch 18/20  Iteration 3048/3560 Training loss: 1.7021 0.0638 sec/batch
Epoch 18/20  Iteration 3049/3560 Training loss: 1.7013 0.0655 sec/batch
Epoch 18/20  Iteration 3050/3560 Training loss: 1.7009 0.0654 sec/batch
Epoch 18/20  Iteration 3051/3560 Training loss: 1.7011 0.0658 sec/batch
Epoch 18/20  Iteration 3052/3