सहायता Kaggle पर TensorFlow साथ ग्रेट बैरियर रीफ की रक्षा चैलेंज में शामिल हों

माइग्रेट जल्दी रुकना

TensorFlow.org पर देखें Google Colab में चलाएं GitHub पर स्रोत देखें नोटबुक डाउनलोड करें

इस नोटबुक दर्शाता है कि कैसे आप को मॉडल प्रशिक्षण जल्दी रोक के साथ पहली बार साथ TensorFlow 1 में सेट कर सकते हैं,, tf.estimator.Estimator और एक प्रारंभिक रोक हुक TensorFlow 2 में Keras एपीआई या एक कस्टम प्रशिक्षण पाश के साथ, और फिर,। प्रारंभिक रोक एक नियमितीकरण तकनीक है जो प्रशिक्षण को रोक देती है, उदाहरण के लिए, सत्यापन हानि एक निश्चित सीमा तक पहुंच जाती है।

TensorFlow 2 में, अर्ली स्टॉपिंग को लागू करने के तीन तरीके हैं:

सेट अप

import time
import numpy as np
import tensorflow as tf
import tensorflow.compat.v1 as tf1
import tensorflow_datasets as tfds

TensorFlow 1: अर्ली स्टॉपिंग हुक और tf.estimator . के साथ अर्ली स्टॉपिंग

MNIST डाटासेट लोड हो रहा है और preprocessing, और साथ प्रयोग किया जा करने के लिए मॉडल परिभाषा के लिए कार्यों को परिभाषित द्वारा शुरू tf.estimator.Estimator :

def normalize_img(image, label):
  return tf.cast(image, tf.float32) / 255., label

def _input_fn():
  ds_train = tfds.load(
    name='mnist',
    split='train',
    shuffle_files=True,
    as_supervised=True)

  ds_train = ds_train.map(
      normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
  ds_train = ds_train.batch(128)
  ds_train = ds_train.repeat(100)
  return ds_train

def _eval_input_fn():
  ds_test = tfds.load(
    name='mnist',
    split='test',
    shuffle_files=True,
    as_supervised=True)
  ds_test = ds_test.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
  ds_test = ds_test.batch(128)
  return ds_test

def _model_fn(features, labels, mode):
  flatten = tf1.layers.Flatten()(features)
  features = tf1.layers.Dense(128, 'relu')(flatten)
  logits = tf1.layers.Dense(10)(features)

  loss = tf1.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
  optimizer = tf1.train.AdagradOptimizer(0.005)
  train_op = optimizer.minimize(loss, global_step=tf1.train.get_global_step())

  return tf1.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

TensorFlow 1 में, जल्दी से एक प्रारंभिक रोक हुक की स्थापना करके काम करता है को रोकने tf.estimator.experimental.make_early_stopping_hook । आप को हुक पारित make_early_stopping_hook के लिए एक पैरामीटर के रूप विधि should_stop_fn , जो किसी भी तर्क के बिना एक समारोह को स्वीकार कर सकते हैं। प्रशिक्षण एक बार बंद हो जाता है should_stop_fn रिटर्न True

निम्नलिखित उदाहरण दर्शाता है कि प्रशिक्षण समय को अधिकतम 20 सेकंड तक सीमित करने वाली प्रारंभिक रोक तकनीक को कैसे लागू किया जाए:

estimator = tf1.estimator.Estimator(model_fn=_model_fn)

start_time = time.time()
max_train_seconds = 20

def should_stop_fn():
  return time.time() - start_time > max_train_seconds

early_stopping_hook = tf1.estimator.experimental.make_early_stopping_hook(
    estimator=estimator,
    should_stop_fn=should_stop_fn,
    run_every_secs=1,
    run_every_steps=None)

train_spec = tf1.estimator.TrainSpec(
    input_fn=_input_fn,
    hooks=[early_stopping_hook])

eval_spec = tf1.estimator.EvalSpec(input_fn=_eval_input_fn)

tf1.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpocmc6_bo
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpocmc6_bo', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/adagrad.py:77: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/adagrad.py:77: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpocmc6_bo/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpocmc6_bo/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 2.3545606, step = 0
INFO:tensorflow:loss = 2.3545606, step = 0
INFO:tensorflow:global_step/sec: 94.5711
INFO:tensorflow:global_step/sec: 94.5711
INFO:tensorflow:loss = 1.3383636, step = 100 (1.060 sec)
INFO:tensorflow:loss = 1.3383636, step = 100 (1.060 sec)
INFO:tensorflow:global_step/sec: 158.428
INFO:tensorflow:global_step/sec: 158.428
INFO:tensorflow:loss = 0.7937969, step = 200 (0.631 sec)
INFO:tensorflow:loss = 0.7937969, step = 200 (0.631 sec)
INFO:tensorflow:global_step/sec: 287.334
INFO:tensorflow:global_step/sec: 287.334
INFO:tensorflow:loss = 0.69060934, step = 300 (0.349 sec)
INFO:tensorflow:loss = 0.69060934, step = 300 (0.349 sec)
INFO:tensorflow:global_step/sec: 286.658
INFO:tensorflow:global_step/sec: 286.658
INFO:tensorflow:loss = 0.59314424, step = 400 (0.349 sec)
INFO:tensorflow:loss = 0.59314424, step = 400 (0.349 sec)
INFO:tensorflow:global_step/sec: 311.591
INFO:tensorflow:global_step/sec: 311.591
INFO:tensorflow:loss = 0.50495726, step = 500 (0.320 sec)
INFO:tensorflow:loss = 0.50495726, step = 500 (0.320 sec)
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 536 vs previous value: 536. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 536 vs previous value: 536. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
INFO:tensorflow:global_step/sec: 538.395
INFO:tensorflow:global_step/sec: 538.395
INFO:tensorflow:loss = 0.43083754, step = 600 (0.186 sec)
INFO:tensorflow:loss = 0.43083754, step = 600 (0.186 sec)
INFO:tensorflow:global_step/sec: 503.72
INFO:tensorflow:global_step/sec: 503.72
INFO:tensorflow:loss = 0.381118, step = 700 (0.198 sec)
INFO:tensorflow:loss = 0.381118, step = 700 (0.198 sec)
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 715 vs previous value: 715. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 715 vs previous value: 715. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
INFO:tensorflow:global_step/sec: 482.019
INFO:tensorflow:global_step/sec: 482.019
INFO:tensorflow:loss = 0.49349022, step = 800 (0.207 sec)
INFO:tensorflow:loss = 0.49349022, step = 800 (0.207 sec)
INFO:tensorflow:global_step/sec: 508.316
INFO:tensorflow:global_step/sec: 508.316
INFO:tensorflow:loss = 0.38730466, step = 900 (0.199 sec)
INFO:tensorflow:loss = 0.38730466, step = 900 (0.199 sec)
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 987 vs previous value: 987. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 987 vs previous value: 987. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
INFO:tensorflow:global_step/sec: 452.89
INFO:tensorflow:global_step/sec: 452.89
INFO:tensorflow:loss = 0.44916487, step = 1000 (0.219 sec)
INFO:tensorflow:loss = 0.44916487, step = 1000 (0.219 sec)
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 1042 vs previous value: 1042. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 1042 vs previous value: 1042. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
INFO:tensorflow:global_step/sec: 519.401
INFO:tensorflow:global_step/sec: 519.401
INFO:tensorflow:loss = 0.44320562, step = 1100 (0.192 sec)
INFO:tensorflow:loss = 0.44320562, step = 1100 (0.192 sec)
INFO:tensorflow:global_step/sec: 510.25
INFO:tensorflow:global_step/sec: 510.25
INFO:tensorflow:loss = 0.3758085, step = 1200 (0.196 sec)
INFO:tensorflow:loss = 0.3758085, step = 1200 (0.196 sec)
INFO:tensorflow:global_step/sec: 518.649
INFO:tensorflow:global_step/sec: 518.649
INFO:tensorflow:loss = 0.46760654, step = 1300 (0.193 sec)
INFO:tensorflow:loss = 0.46760654, step = 1300 (0.193 sec)
INFO:tensorflow:global_step/sec: 474.056
INFO:tensorflow:global_step/sec: 474.056
INFO:tensorflow:loss = 0.29544568, step = 1400 (0.211 sec)
INFO:tensorflow:loss = 0.29544568, step = 1400 (0.211 sec)
INFO:tensorflow:global_step/sec: 461.406
INFO:tensorflow:global_step/sec: 461.406
INFO:tensorflow:loss = 0.28616875, step = 1500 (0.217 sec)
INFO:tensorflow:loss = 0.28616875, step = 1500 (0.217 sec)
INFO:tensorflow:global_step/sec: 486.2
INFO:tensorflow:global_step/sec: 486.2
INFO:tensorflow:loss = 0.4114887, step = 1600 (0.206 sec)
INFO:tensorflow:loss = 0.4114887, step = 1600 (0.206 sec)
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 1678 vs previous value: 1678. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 1678 vs previous value: 1678. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.
INFO:tensorflow:global_step/sec: 507.701
INFO:tensorflow:global_step/sec: 507.701
INFO:tensorflow:loss = 0.35298553, step = 1700 (0.197 sec)
INFO:tensorflow:loss = 0.35298553, step = 1700 (0.197 sec)
INFO:tensorflow:global_step/sec: 490.541
INFO:tensorflow:global_step/sec: 490.541
INFO:tensorflow:loss = 0.3363277, step = 1800 (0.204 sec)
INFO:tensorflow:loss = 0.3363277, step = 1800 (0.204 sec)
INFO:tensorflow:global_step/sec: 460.083
INFO:tensorflow:global_step/sec: 460.083
INFO:tensorflow:loss = 0.50634325, step = 1900 (0.217 sec)
INFO:tensorflow:loss = 0.50634325, step = 1900 (0.217 sec)
INFO:tensorflow:global_step/sec: 436.782
INFO:tensorflow:global_step/sec: 436.782
INFO:tensorflow:loss = 0.2063987, step = 2000 (0.229 sec)
INFO:tensorflow:loss = 0.2063987, step = 2000 (0.229 sec)
INFO:tensorflow:global_step/sec: 475.841
INFO:tensorflow:global_step/sec: 475.841
INFO:tensorflow:loss = 0.27246287, step = 2100 (0.210 sec)
INFO:tensorflow:loss = 0.27246287, step = 2100 (0.210 sec)
INFO:tensorflow:global_step/sec: 483.322
INFO:tensorflow:global_step/sec: 483.322
INFO:tensorflow:loss = 0.31674564, step = 2200 (0.207 sec)
INFO:tensorflow:loss = 0.31674564, step = 2200 (0.207 sec)
INFO:tensorflow:global_step/sec: 442.257
INFO:tensorflow:global_step/sec: 442.257
INFO:tensorflow:loss = 0.3334998, step = 2300 (0.226 sec)
INFO:tensorflow:loss = 0.3334998, step = 2300 (0.226 sec)
INFO:tensorflow:global_step/sec: 476.38
INFO:tensorflow:global_step/sec: 476.38
INFO:tensorflow:loss = 0.2549953, step = 2400 (0.210 sec)
INFO:tensorflow:loss = 0.2549953, step = 2400 (0.210 sec)
INFO:tensorflow:global_step/sec: 467.543
INFO:tensorflow:global_step/sec: 467.543
INFO:tensorflow:loss = 0.21111101, step = 2500 (0.214 sec)
INFO:tensorflow:loss = 0.21111101, step = 2500 (0.214 sec)
INFO:tensorflow:global_step/sec: 497.051
INFO:tensorflow:global_step/sec: 497.051
INFO:tensorflow:loss = 0.15878338, step = 2600 (0.201 sec)
INFO:tensorflow:loss = 0.15878338, step = 2600 (0.201 sec)
INFO:tensorflow:global_step/sec: 461.785
INFO:tensorflow:global_step/sec: 461.785
INFO:tensorflow:loss = 0.31587577, step = 2700 (0.219 sec)
INFO:tensorflow:loss = 0.31587577, step = 2700 (0.219 sec)
INFO:tensorflow:global_step/sec: 493.743
INFO:tensorflow:global_step/sec: 493.743
INFO:tensorflow:loss = 0.47478187, step = 2800 (0.200 sec)
INFO:tensorflow:loss = 0.47478187, step = 2800 (0.200 sec)
INFO:tensorflow:global_step/sec: 463.477
INFO:tensorflow:global_step/sec: 463.477
INFO:tensorflow:loss = 0.2499526, step = 2900 (0.216 sec)
INFO:tensorflow:loss = 0.2499526, step = 2900 (0.216 sec)
INFO:tensorflow:global_step/sec: 538.27
INFO:tensorflow:global_step/sec: 538.27
INFO:tensorflow:loss = 0.34210858, step = 3000 (0.186 sec)
INFO:tensorflow:loss = 0.34210858, step = 3000 (0.186 sec)
INFO:tensorflow:global_step/sec: 508.741
INFO:tensorflow:global_step/sec: 508.741
INFO:tensorflow:loss = 0.2128592, step = 3100 (0.197 sec)
INFO:tensorflow:loss = 0.2128592, step = 3100 (0.197 sec)
INFO:tensorflow:global_step/sec: 519.319
INFO:tensorflow:global_step/sec: 519.319
INFO:tensorflow:loss = 0.40954083, step = 3200 (0.192 sec)
INFO:tensorflow:loss = 0.40954083, step = 3200 (0.192 sec)
INFO:tensorflow:global_step/sec: 468.989
INFO:tensorflow:global_step/sec: 468.989
INFO:tensorflow:loss = 0.34270883, step = 3300 (0.213 sec)
INFO:tensorflow:loss = 0.34270883, step = 3300 (0.213 sec)
INFO:tensorflow:global_step/sec: 479.856
INFO:tensorflow:global_step/sec: 479.856
INFO:tensorflow:loss = 0.26599607, step = 3400 (0.209 sec)
INFO:tensorflow:loss = 0.26599607, step = 3400 (0.209 sec)
INFO:tensorflow:global_step/sec: 495.76
INFO:tensorflow:global_step/sec: 495.76
INFO:tensorflow:loss = 0.21713805, step = 3500 (0.201 sec)
INFO:tensorflow:loss = 0.21713805, step = 3500 (0.201 sec)
INFO:tensorflow:global_step/sec: 440.282
INFO:tensorflow:global_step/sec: 440.282
INFO:tensorflow:loss = 0.22268976, step = 3600 (0.228 sec)
INFO:tensorflow:loss = 0.22268976, step = 3600 (0.228 sec)
INFO:tensorflow:global_step/sec: 495.629
INFO:tensorflow:global_step/sec: 495.629
INFO:tensorflow:loss = 0.28974164, step = 3700 (0.201 sec)
INFO:tensorflow:loss = 0.28974164, step = 3700 (0.201 sec)
INFO:tensorflow:global_step/sec: 468.695
INFO:tensorflow:global_step/sec: 468.695
INFO:tensorflow:loss = 0.37919793, step = 3800 (0.214 sec)
INFO:tensorflow:loss = 0.37919793, step = 3800 (0.214 sec)
INFO:tensorflow:global_step/sec: 529.005
INFO:tensorflow:global_step/sec: 529.005
INFO:tensorflow:loss = 0.23738712, step = 3900 (0.189 sec)
INFO:tensorflow:loss = 0.23738712, step = 3900 (0.189 sec)
INFO:tensorflow:global_step/sec: 494.809
INFO:tensorflow:global_step/sec: 494.809
INFO:tensorflow:loss = 0.29650036, step = 4000 (0.204 sec)
INFO:tensorflow:loss = 0.29650036, step = 4000 (0.204 sec)
INFO:tensorflow:global_step/sec: 525.629
INFO:tensorflow:global_step/sec: 525.629
INFO:tensorflow:loss = 0.20826155, step = 4100 (0.188 sec)
INFO:tensorflow:loss = 0.20826155, step = 4100 (0.188 sec)
INFO:tensorflow:global_step/sec: 509.573
INFO:tensorflow:global_step/sec: 509.573
INFO:tensorflow:loss = 0.26417816, step = 4200 (0.196 sec)
INFO:tensorflow:loss = 0.26417816, step = 4200 (0.196 sec)
INFO:tensorflow:global_step/sec: 472.845
INFO:tensorflow:global_step/sec: 472.845
INFO:tensorflow:loss = 0.31241363, step = 4300 (0.212 sec)
INFO:tensorflow:loss = 0.31241363, step = 4300 (0.212 sec)
INFO:tensorflow:global_step/sec: 510.868
INFO:tensorflow:global_step/sec: 510.868
INFO:tensorflow:loss = 0.32773697, step = 4400 (0.195 sec)
INFO:tensorflow:loss = 0.32773697, step = 4400 (0.195 sec)
INFO:tensorflow:global_step/sec: 492.967
INFO:tensorflow:global_step/sec: 492.967
INFO:tensorflow:loss = 0.28609803, step = 4500 (0.203 sec)
INFO:tensorflow:loss = 0.28609803, step = 4500 (0.203 sec)
INFO:tensorflow:global_step/sec: 507.394
INFO:tensorflow:global_step/sec: 507.394
INFO:tensorflow:loss = 0.32142323, step = 4600 (0.197 sec)
INFO:tensorflow:loss = 0.32142323, step = 4600 (0.197 sec)
INFO:tensorflow:global_step/sec: 475.176
INFO:tensorflow:global_step/sec: 475.176
INFO:tensorflow:loss = 0.14882785, step = 4700 (0.211 sec)
INFO:tensorflow:loss = 0.14882785, step = 4700 (0.211 sec)
INFO:tensorflow:global_step/sec: 503.718
INFO:tensorflow:global_step/sec: 503.718
INFO:tensorflow:loss = 0.312344, step = 4800 (0.198 sec)
INFO:tensorflow:loss = 0.312344, step = 4800 (0.198 sec)
INFO:tensorflow:global_step/sec: 497.659
INFO:tensorflow:global_step/sec: 497.659
INFO:tensorflow:loss = 0.37370217, step = 4900 (0.201 sec)
INFO:tensorflow:loss = 0.37370217, step = 4900 (0.201 sec)
INFO:tensorflow:global_step/sec: 477.736
INFO:tensorflow:global_step/sec: 477.736
INFO:tensorflow:loss = 0.2663591, step = 5000 (0.209 sec)
INFO:tensorflow:loss = 0.2663591, step = 5000 (0.209 sec)
INFO:tensorflow:global_step/sec: 496.559
INFO:tensorflow:global_step/sec: 496.559
INFO:tensorflow:loss = 0.34745598, step = 5100 (0.202 sec)
INFO:tensorflow:loss = 0.34745598, step = 5100 (0.202 sec)
INFO:tensorflow:global_step/sec: 475.989
INFO:tensorflow:global_step/sec: 475.989
INFO:tensorflow:loss = 0.21809828, step = 5200 (0.210 sec)
INFO:tensorflow:loss = 0.21809828, step = 5200 (0.210 sec)
INFO:tensorflow:global_step/sec: 474.464
INFO:tensorflow:global_step/sec: 474.464
INFO:tensorflow:loss = 0.2474105, step = 5300 (0.211 sec)
INFO:tensorflow:loss = 0.2474105, step = 5300 (0.211 sec)
INFO:tensorflow:global_step/sec: 488.774
INFO:tensorflow:global_step/sec: 488.774
INFO:tensorflow:loss = 0.1611641, step = 5400 (0.204 sec)
INFO:tensorflow:loss = 0.1611641, step = 5400 (0.204 sec)
INFO:tensorflow:global_step/sec: 504.942
INFO:tensorflow:global_step/sec: 504.942
INFO:tensorflow:loss = 0.2306528, step = 5500 (0.198 sec)
INFO:tensorflow:loss = 0.2306528, step = 5500 (0.198 sec)
INFO:tensorflow:global_step/sec: 514.058
INFO:tensorflow:global_step/sec: 514.058
INFO:tensorflow:loss = 0.20716992, step = 5600 (0.195 sec)
INFO:tensorflow:loss = 0.20716992, step = 5600 (0.195 sec)
INFO:tensorflow:global_step/sec: 458.899
INFO:tensorflow:global_step/sec: 458.899
INFO:tensorflow:loss = 0.16730343, step = 5700 (0.217 sec)
INFO:tensorflow:loss = 0.16730343, step = 5700 (0.217 sec)
INFO:tensorflow:global_step/sec: 495.197
INFO:tensorflow:global_step/sec: 495.197
INFO:tensorflow:loss = 0.2906361, step = 5800 (0.202 sec)
INFO:tensorflow:loss = 0.2906361, step = 5800 (0.202 sec)
INFO:tensorflow:global_step/sec: 482.244
INFO:tensorflow:global_step/sec: 482.244
INFO:tensorflow:loss = 0.24669808, step = 5900 (0.207 sec)
INFO:tensorflow:loss = 0.24669808, step = 5900 (0.207 sec)
INFO:tensorflow:global_step/sec: 484.946
INFO:tensorflow:global_step/sec: 484.946
INFO:tensorflow:loss = 0.26403594, step = 6000 (0.207 sec)
INFO:tensorflow:loss = 0.26403594, step = 6000 (0.207 sec)
INFO:tensorflow:global_step/sec: 486.74
INFO:tensorflow:global_step/sec: 486.74
INFO:tensorflow:loss = 0.19804293, step = 6100 (0.206 sec)
INFO:tensorflow:loss = 0.19804293, step = 6100 (0.206 sec)
INFO:tensorflow:global_step/sec: 436.727
INFO:tensorflow:global_step/sec: 436.727
INFO:tensorflow:loss = 0.25344175, step = 6200 (0.229 sec)
INFO:tensorflow:loss = 0.25344175, step = 6200 (0.229 sec)
INFO:tensorflow:global_step/sec: 428.73
INFO:tensorflow:global_step/sec: 428.73
INFO:tensorflow:loss = 0.2430937, step = 6300 (0.232 sec)
INFO:tensorflow:loss = 0.2430937, step = 6300 (0.232 sec)
INFO:tensorflow:global_step/sec: 449.706
INFO:tensorflow:global_step/sec: 449.706
INFO:tensorflow:loss = 0.2842306, step = 6400 (0.222 sec)
INFO:tensorflow:loss = 0.2842306, step = 6400 (0.222 sec)
INFO:tensorflow:global_step/sec: 440.873
INFO:tensorflow:global_step/sec: 440.873
INFO:tensorflow:loss = 0.2641199, step = 6500 (0.227 sec)
INFO:tensorflow:loss = 0.2641199, step = 6500 (0.227 sec)
INFO:tensorflow:global_step/sec: 424.092
INFO:tensorflow:global_step/sec: 424.092
INFO:tensorflow:loss = 0.19028814, step = 6600 (0.237 sec)
INFO:tensorflow:loss = 0.19028814, step = 6600 (0.237 sec)
INFO:tensorflow:global_step/sec: 450.352
INFO:tensorflow:global_step/sec: 450.352
INFO:tensorflow:loss = 0.24667627, step = 6700 (0.221 sec)
INFO:tensorflow:loss = 0.24667627, step = 6700 (0.221 sec)
INFO:tensorflow:global_step/sec: 462.774
INFO:tensorflow:global_step/sec: 462.774
INFO:tensorflow:loss = 0.40046322, step = 6800 (0.216 sec)
INFO:tensorflow:loss = 0.40046322, step = 6800 (0.216 sec)
INFO:tensorflow:global_step/sec: 460.854
INFO:tensorflow:global_step/sec: 460.854
INFO:tensorflow:loss = 0.14105138, step = 6900 (0.217 sec)
INFO:tensorflow:loss = 0.14105138, step = 6900 (0.217 sec)
INFO:tensorflow:Requesting early stopping at global step 6916
INFO:tensorflow:Requesting early stopping at global step 6916
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6917...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6917...
INFO:tensorflow:Saving checkpoints for 6917 into /tmp/tmpocmc6_bo/model.ckpt.
INFO:tensorflow:Saving checkpoints for 6917 into /tmp/tmpocmc6_bo/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6917...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6917...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:07:35
INFO:tensorflow:Starting evaluation at 2021-09-22T20:07:35
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpocmc6_bo/model.ckpt-6917
INFO:tensorflow:Restoring parameters from /tmp/tmpocmc6_bo/model.ckpt-6917
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [10/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [20/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [30/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [40/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [50/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [60/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Evaluation [70/100]
INFO:tensorflow:Inference Time : 0.79520s
INFO:tensorflow:Inference Time : 0.79520s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:07:36
INFO:tensorflow:Finished evaluation at 2021-09-22-20:07:36
INFO:tensorflow:Saving dict for global step 6917: global_step = 6917, loss = 0.227278
INFO:tensorflow:Saving dict for global step 6917: global_step = 6917, loss = 0.227278
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6917: /tmp/tmpocmc6_bo/model.ckpt-6917
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6917: /tmp/tmpocmc6_bo/model.ckpt-6917
INFO:tensorflow:Loss for final step: 0.13882703.
INFO:tensorflow:Loss for final step: 0.13882703.
({'loss': 0.227278, 'global_step': 6917}, [])

TensorFlow 2: बिल्ट-इन कॉलबैक और Model.fit के साथ जल्दी रुकना

एमएनआईएसटी डेटासेट और एक साधारण केरस मॉडल तैयार करें:

(ds_train, ds_test), ds_info = tfds.load(
    'mnist',
    split=['train', 'test'],
    shuffle_files=True,
    as_supervised=True,
    with_info=True,
)

ds_train = ds_train.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_train = ds_train.batch(128)

ds_test = ds_test.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_test = ds_test.batch(128)

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10)
])

model.compile(
    optimizer=tf.keras.optimizers.Adam(0.005),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)

TensorFlow 2, में आप में निर्मित Keras का उपयोग करते हैं Model.fit (या Model.evaluate ), तो आप कॉन्फ़िगर कर सकते हैं जल्दी एक अंतर्निहित callback- पास करके रोक tf.keras.callbacks.EarlyStopping करने वाली callbacks के पैरामीटर Model.fit

EarlyStopping कॉलबैक पर नज़र रखता है एक उपयोगकर्ता द्वारा निर्दिष्ट मीट्रिक और समाप्त होता है प्रशिक्षण जब यह सुधार के बंद हो जाता है। (चेक प्रशिक्षण और साथ मूल्यांकन में निर्मित तरीकों या एपीआई डॉक्स अधिक जानकारी के लिए।)

नीचे एक प्रारंभिक रोक कॉलबैक का एक उदाहरण है कि नज़र रखता घटाने और बंद हो जाता है कि कोई भी अवधियों को सुधार दिखाने के लिए सेट कर दिया जाता की संख्या के बाद प्रशिक्षण है 3 ( patience ):

callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)

# Only around 25 epochs are run during training, instead of 100.
history = model.fit(
    ds_train,
    epochs=100,
    validation_data=ds_test,
    callbacks=[callback]
)

len(history.history['loss'])
Epoch 1/100
469/469 [==============================] - 5s 8ms/step - loss: 0.2371 - sparse_categorical_accuracy: 0.9293 - val_loss: 0.1334 - val_sparse_categorical_accuracy: 0.9611
Epoch 2/100
469/469 [==============================] - 1s 3ms/step - loss: 0.1028 - sparse_categorical_accuracy: 0.9686 - val_loss: 0.1062 - val_sparse_categorical_accuracy: 0.9667
Epoch 3/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0703 - sparse_categorical_accuracy: 0.9783 - val_loss: 0.0993 - val_sparse_categorical_accuracy: 0.9707
Epoch 4/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0552 - sparse_categorical_accuracy: 0.9822 - val_loss: 0.1040 - val_sparse_categorical_accuracy: 0.9680
Epoch 5/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0420 - sparse_categorical_accuracy: 0.9865 - val_loss: 0.1033 - val_sparse_categorical_accuracy: 0.9716
Epoch 6/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0387 - sparse_categorical_accuracy: 0.9871 - val_loss: 0.1167 - val_sparse_categorical_accuracy: 0.9691
Epoch 7/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0321 - sparse_categorical_accuracy: 0.9893 - val_loss: 0.1396 - val_sparse_categorical_accuracy: 0.9672
Epoch 8/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0285 - sparse_categorical_accuracy: 0.9902 - val_loss: 0.1397 - val_sparse_categorical_accuracy: 0.9671
Epoch 9/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0263 - sparse_categorical_accuracy: 0.9915 - val_loss: 0.1296 - val_sparse_categorical_accuracy: 0.9715
Epoch 10/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0250 - sparse_categorical_accuracy: 0.9915 - val_loss: 0.1440 - val_sparse_categorical_accuracy: 0.9715
Epoch 11/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0274 - sparse_categorical_accuracy: 0.9910 - val_loss: 0.1439 - val_sparse_categorical_accuracy: 0.9710
Epoch 12/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0241 - sparse_categorical_accuracy: 0.9923 - val_loss: 0.1429 - val_sparse_categorical_accuracy: 0.9718
Epoch 13/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0205 - sparse_categorical_accuracy: 0.9929 - val_loss: 0.1451 - val_sparse_categorical_accuracy: 0.9753
Epoch 14/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0196 - sparse_categorical_accuracy: 0.9936 - val_loss: 0.1562 - val_sparse_categorical_accuracy: 0.9750
Epoch 15/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0214 - sparse_categorical_accuracy: 0.9930 - val_loss: 0.1531 - val_sparse_categorical_accuracy: 0.9748
Epoch 16/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0178 - sparse_categorical_accuracy: 0.9941 - val_loss: 0.1712 - val_sparse_categorical_accuracy: 0.9731
Epoch 17/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0177 - sparse_categorical_accuracy: 0.9947 - val_loss: 0.1715 - val_sparse_categorical_accuracy: 0.9755
Epoch 18/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0141 - sparse_categorical_accuracy: 0.9952 - val_loss: 0.1826 - val_sparse_categorical_accuracy: 0.9730
Epoch 19/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0188 - sparse_categorical_accuracy: 0.9942 - val_loss: 0.1919 - val_sparse_categorical_accuracy: 0.9732
Epoch 20/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0190 - sparse_categorical_accuracy: 0.9944 - val_loss: 0.1703 - val_sparse_categorical_accuracy: 0.9777
Epoch 21/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0153 - sparse_categorical_accuracy: 0.9951 - val_loss: 0.1725 - val_sparse_categorical_accuracy: 0.9764
21

TensorFlow 2: कस्टम कॉलबैक और Model.fit के साथ जल्दी रुकना

तुम भी एक को लागू कर सकते कस्टम जल्दी कॉलबैक रोक है, जो भी करने के लिए पारित किया जा सकता callbacks के पैरामीटर Model.fit (या Model.evaluate )।

इस उदाहरण में, प्रशिक्षण प्रक्रिया एक बार बंद कर दिया है self.model.stop_training होने के लिए सेट कर दिया जाता True :

class LimitTrainingTime(tf.keras.callbacks.Callback):
  def __init__(self, max_time_s):
    super().__init__()
    self.max_time_s = max_time_s
    self.start_time = None

  def on_train_begin(self, logs):
    self.start_time = time.time()

  def on_train_batch_end(self, batch, logs):
    now = time.time()
    if now - self.start_time >  self.max_time_s:
      self.model.stop_training = True
# Limit the training time to 30 seconds.
callback = LimitTrainingTime(30)
history = model.fit(
    ds_train,
    epochs=100,
    validation_data=ds_test,
    callbacks=[callback]
)
len(history.history['loss'])
Epoch 1/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0131 - sparse_categorical_accuracy: 0.9961 - val_loss: 0.1911 - val_sparse_categorical_accuracy: 0.9749
Epoch 2/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0133 - sparse_categorical_accuracy: 0.9958 - val_loss: 0.1999 - val_sparse_categorical_accuracy: 0.9755
Epoch 3/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0153 - sparse_categorical_accuracy: 0.9952 - val_loss: 0.1927 - val_sparse_categorical_accuracy: 0.9770
Epoch 4/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0145 - sparse_categorical_accuracy: 0.9957 - val_loss: 0.2279 - val_sparse_categorical_accuracy: 0.9753
Epoch 5/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0141 - sparse_categorical_accuracy: 0.9959 - val_loss: 0.2272 - val_sparse_categorical_accuracy: 0.9755
Epoch 6/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0132 - sparse_categorical_accuracy: 0.9962 - val_loss: 0.2352 - val_sparse_categorical_accuracy: 0.9747
Epoch 7/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0144 - sparse_categorical_accuracy: 0.9960 - val_loss: 0.2421 - val_sparse_categorical_accuracy: 0.9734
Epoch 8/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0128 - sparse_categorical_accuracy: 0.9964 - val_loss: 0.2260 - val_sparse_categorical_accuracy: 0.9785
Epoch 9/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0129 - sparse_categorical_accuracy: 0.9965 - val_loss: 0.2472 - val_sparse_categorical_accuracy: 0.9752
Epoch 10/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0143 - sparse_categorical_accuracy: 0.9961 - val_loss: 0.2166 - val_sparse_categorical_accuracy: 0.9768
Epoch 11/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0145 - sparse_categorical_accuracy: 0.9963 - val_loss: 0.2289 - val_sparse_categorical_accuracy: 0.9781
Epoch 12/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0119 - sparse_categorical_accuracy: 0.9968 - val_loss: 0.2310 - val_sparse_categorical_accuracy: 0.9777
Epoch 13/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0144 - sparse_categorical_accuracy: 0.9966 - val_loss: 0.2617 - val_sparse_categorical_accuracy: 0.9781
Epoch 14/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0119 - sparse_categorical_accuracy: 0.9972 - val_loss: 0.3007 - val_sparse_categorical_accuracy: 0.9754
Epoch 15/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0150 - sparse_categorical_accuracy: 0.9966 - val_loss: 0.3014 - val_sparse_categorical_accuracy: 0.9767
Epoch 16/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0143 - sparse_categorical_accuracy: 0.9963 - val_loss: 0.2815 - val_sparse_categorical_accuracy: 0.9750
Epoch 17/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0129 - sparse_categorical_accuracy: 0.9967 - val_loss: 0.2606 - val_sparse_categorical_accuracy: 0.9765
Epoch 18/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0103 - sparse_categorical_accuracy: 0.9975 - val_loss: 0.2602 - val_sparse_categorical_accuracy: 0.9777
Epoch 19/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0098 - sparse_categorical_accuracy: 0.9979 - val_loss: 0.2594 - val_sparse_categorical_accuracy: 0.9780
Epoch 20/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0156 - sparse_categorical_accuracy: 0.9965 - val_loss: 0.3008 - val_sparse_categorical_accuracy: 0.9755
Epoch 21/100
469/469 [==============================] - 1s 3ms/step - loss: 0.0110 - sparse_categorical_accuracy: 0.9974 - val_loss: 0.2662 - val_sparse_categorical_accuracy: 0.9765
Epoch 22/100
469/469 [==============================] - 1s 1ms/step - loss: 0.0083 - sparse_categorical_accuracy: 0.9978 - val_loss: 0.2587 - val_sparse_categorical_accuracy: 0.9797
22

TensorFlow 2: कस्टम ट्रेनिंग लूप के साथ जल्दी रुकना

TensorFlow 2 में, आप जल्दी एक में रोक लागू कर सकते हैं कस्टम प्रशिक्षण पाश अगर आप प्रशिक्षण और साथ का मूल्यांकन नहीं कर रहे हैं में निर्मित Keras तरीकों

एक और सरल मॉडल, एक अनुकूलक, एक हानि फ़ंक्शन और मेट्रिक्स को परिभाषित करने के लिए केरस एपीआई का उपयोग करके प्रारंभ करें:

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10)
])

optimizer = tf.keras.optimizers.Adam(0.005)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

train_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy()
train_loss_metric = tf.keras.metrics.SparseCategoricalCrossentropy()
val_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy()
val_loss_metric = tf.keras.metrics.SparseCategoricalCrossentropy()

पैरामीटर अद्यतन कार्यों को परिभाषित tf.GradientTape साथ और @tf.function डेकोरेटर एक speedup के लिए :

@tf.function
def train_step(x, y):
  with tf.GradientTape() as tape:
      logits = model(x, training=True)
      loss_value = loss_fn(y, logits)
  grads = tape.gradient(loss_value, model.trainable_weights)
  optimizer.apply_gradients(zip(grads, model.trainable_weights))
  train_acc_metric.update_state(y, logits)
  train_loss_metric.update_state(y, logits)
  return loss_value

@tf.function
def test_step(x, y):
  logits = model(x, training=False)
  val_acc_metric.update_state(y, logits)
  val_loss_metric.update_state(y, logits)

इसके बाद, एक कस्टम प्रशिक्षण लूप लिखें, जहां आप अपने प्रारंभिक रोक नियम को मैन्युअल रूप से लागू कर सकते हैं।

नीचे दिया गया उदाहरण दिखाता है कि प्रशिक्षण को कैसे रोका जाए जब एक निश्चित संख्या में सत्यापन हानि में सुधार नहीं होता है:

epochs = 100
patience = 5
wait = 0
best = 0

for epoch in range(epochs):
    print("\nStart of epoch %d" % (epoch,))
    start_time = time.time()

    for step, (x_batch_train, y_batch_train) in enumerate(ds_train):
      loss_value = train_step(x_batch_train, y_batch_train)
      if step % 200 == 0:
        print("Training loss at step %d: %.4f" % (step, loss_value.numpy()))
        print("Seen so far: %s samples" % ((step + 1) * 128))        
    train_acc = train_acc_metric.result()
    train_loss = train_loss_metric.result()
    train_acc_metric.reset_states()
    train_loss_metric.reset_states()
    print("Training acc over epoch: %.4f" % (train_acc.numpy()))

    for x_batch_val, y_batch_val in ds_test:
      test_step(x_batch_val, y_batch_val)
    val_acc = val_acc_metric.result()
    val_loss = val_loss_metric.result()
    val_acc_metric.reset_states()
    val_loss_metric.reset_states()
    print("Validation acc: %.4f" % (float(val_acc),))
    print("Time taken: %.2fs" % (time.time() - start_time))

    # The early stopping strategy: stop the training if `val_loss` does not
    # decrease over a certain number of epochs.
    wait += 1
    if val_loss > best:
      best = val_loss
      wait = 0
    if wait >= patience:
      break
Start of epoch 0
Training loss at step 0: 2.3073
Seen so far: 128 samples
Training loss at step 200: 0.2164
Seen so far: 25728 samples
Training loss at step 400: 0.2186
Seen so far: 51328 samples
Training acc over epoch: 0.9321
Validation acc: 0.9644
Time taken: 1.73s

Start of epoch 1
Training loss at step 0: 0.0733
Seen so far: 128 samples
Training loss at step 200: 0.1581
Seen so far: 25728 samples
Training loss at step 400: 0.1625
Seen so far: 51328 samples
Training acc over epoch: 0.9704
Validation acc: 0.9681
Time taken: 1.23s

Start of epoch 2
Training loss at step 0: 0.0501
Seen so far: 128 samples
Training loss at step 200: 0.1389
Seen so far: 25728 samples
Training loss at step 400: 0.1495
Seen so far: 51328 samples
Training acc over epoch: 0.9779
Validation acc: 0.9703
Time taken: 1.17s

Start of epoch 3
Training loss at step 0: 0.0513
Seen so far: 128 samples
Training loss at step 200: 0.0638
Seen so far: 25728 samples
Training loss at step 400: 0.0930
Seen so far: 51328 samples
Training acc over epoch: 0.9830
Validation acc: 0.9719
Time taken: 1.20s

Start of epoch 4
Training loss at step 0: 0.0251
Seen so far: 128 samples
Training loss at step 200: 0.0482
Seen so far: 25728 samples
Training loss at step 400: 0.0872
Seen so far: 51328 samples
Training acc over epoch: 0.9849
Validation acc: 0.9672
Time taken: 1.18s

Start of epoch 5
Training loss at step 0: 0.0417
Seen so far: 128 samples
Training loss at step 200: 0.0302
Seen so far: 25728 samples
Training loss at step 400: 0.0362
Seen so far: 51328 samples
Training acc over epoch: 0.9878
Validation acc: 0.9703
Time taken: 1.21s

अगला कदम