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TensorBoard is a built-in tool for providing measurements and visualizations in TensorFlow. Common machine learning experiment metrics, such as accuracy and loss, can be tracked and displayed in TensorBoard. TensorBoard is compatible with TensorFlow 1 and 2 code.
In TensorFlow 1, tf.estimator.Estimator
saves summaries for TensorBoard by default. In comparison, in TensorFlow 2, summaries can be saved using a tf.keras.callbacks.TensorBoard
callback.
This guide demonstrates how to use TensorBoard, first, in TensorFlow 1 with Estimators, and then, how to carry out the equivalent process in TensorFlow 2.
Setup
import tensorflow.compat.v1 as tf1
import tensorflow as tf
import tempfile
import numpy as np
import datetime
%load_ext tensorboard
2023-02-10 02:22:08.986271: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory 2023-02-10 02:22:08.986358: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 2023-02-10 02:22:08.986367: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
mnist = tf.keras.datasets.mnist # The MNIST dataset.
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz 11490434/11490434 [==============================] - 0s 0us/step
TensorFlow 1: TensorBoard with tf.estimator
In this TensorFlow 1 example, you instantiate a tf.estimator.DNNClassifier
, train and evaluate it on the MNIST dataset, and use TensorBoard to display the metrics:
%reload_ext tensorboard
feature_columns = [tf1.feature_column.numeric_column("x", shape=[28, 28])]
config = tf1.estimator.RunConfig(save_summary_steps=1,
save_checkpoints_steps=1)
path = tempfile.mkdtemp()
classifier = tf1.estimator.DNNClassifier(
feature_columns=feature_columns,
hidden_units=[256, 32],
optimizer=tf1.train.AdamOptimizer(0.001),
n_classes=10,
dropout=0.1,
model_dir=path,
config = config
)
train_input_fn = tf1.estimator.inputs.numpy_input_fn(
x={"x": x_train},
y=y_train.astype(np.int32),
num_epochs=10,
batch_size=50,
shuffle=True,
)
test_input_fn = tf1.estimator.inputs.numpy_input_fn(
x={"x": x_test},
y=y_test.astype(np.int32),
num_epochs=10,
shuffle=False
)
train_spec = tf1.estimator.TrainSpec(input_fn=train_input_fn, max_steps=10)
eval_spec = tf1.estimator.EvalSpec(input_fn=test_input_fn,
steps=10,
throttle_secs=0)
tf1.estimator.train_and_evaluate(estimator=classifier,
train_spec=train_spec,
eval_spec=eval_spec)
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpt0r_lxj6', '_tf_random_seed': None, '_save_summary_steps': 1, '_save_checkpoints_steps': 1, '_save_checkpoints_secs': None, '_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} WARNING:tensorflow:From /tmpfs/tmp/ipykernel_8757/2752664473.py:20: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_8757/2752664473.py:20: The name tf.estimator.inputs.numpy_input_fn is deprecated. Please use tf.compat.v1.estimator.inputs.numpy_input_fn instead. 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 1 or save_checkpoints_secs None. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/training_util.py:396: 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. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:60: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:491: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:910: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version. Instructions for updating: To construct input pipelines, use the `tf.data` module. 2023-02-10 02:22:14.735807: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1: type_id: TFT_OPTIONAL args { type_id: TFT_PRODUCT args { type_id: TFT_TENSOR args { type_id: TFT_INT64 } } } is neither a subtype nor a supertype of the combined inputs preceding it: type_id: TFT_OPTIONAL args { type_id: TFT_PRODUCT args { type_id: TFT_TENSOR args { type_id: TFT_INT32 } } } while inferring type of node 'dnn/zero_fraction/cond/output/_18' INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1... INFO:tensorflow:Saving checkpoints for 1 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:17 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-1 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.28990s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:17 INFO:tensorflow:Saving dict for global step 1: accuracy = 0.12578125, average_loss = 2.2894325, global_step = 1, loss = 293.04736 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-1 INFO:tensorflow:loss = 121.82622, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2... INFO:tensorflow:Saving checkpoints for 2 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:17 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-2 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.28622s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:18 INFO:tensorflow:Saving dict for global step 2: accuracy = 0.18671875, average_loss = 2.2372622, global_step = 2, loss = 286.36957 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-2 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3... INFO:tensorflow:Saving checkpoints for 3 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:18 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-3 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.27657s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:18 INFO:tensorflow:Saving dict for global step 3: accuracy = 0.25, average_loss = 2.1874187, global_step = 3, loss = 279.9896 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-3 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4... INFO:tensorflow:Saving checkpoints for 4 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:18 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-4 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.28320s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:19 INFO:tensorflow:Saving dict for global step 4: accuracy = 0.33125, average_loss = 2.130702, global_step = 4, loss = 272.72986 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-4 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5... INFO:tensorflow:Saving checkpoints for 5 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/saver.py:1064: remove_checkpoint (from tensorflow.python.checkpoint.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to delete files with this prefix. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:19 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-5 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.28319s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:19 INFO:tensorflow:Saving dict for global step 5: accuracy = 0.39375, average_loss = 2.0748143, global_step = 5, loss = 265.57623 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-5 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6... INFO:tensorflow:Saving checkpoints for 6 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:20 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-6 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.28250s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:20 INFO:tensorflow:Saving dict for global step 6: accuracy = 0.4046875, average_loss = 2.0199292, global_step = 6, loss = 258.55093 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-6 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7... INFO:tensorflow:Saving checkpoints for 7 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:20 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-7 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.27775s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:20 INFO:tensorflow:Saving dict for global step 7: accuracy = 0.4015625, average_loss = 1.9654633, global_step = 7, loss = 251.5793 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 7: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-7 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8... INFO:tensorflow:Saving checkpoints for 8 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:21 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-8 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.27721s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:21 INFO:tensorflow:Saving dict for global step 8: accuracy = 0.41796875, average_loss = 1.9034822, global_step = 8, loss = 243.64572 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 8: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-8 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9... INFO:tensorflow:Saving checkpoints for 9 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:21 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-9 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.28626s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:21 INFO:tensorflow:Saving dict for global step 9: accuracy = 0.4328125, average_loss = 1.8404834, global_step = 9, loss = 235.58188 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 9: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-9 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpt0r_lxj6/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2023-02-10T02:22:22 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-10 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 0.28145s INFO:tensorflow:Finished evaluation at 2023-02-10-02:22:22 INFO:tensorflow:Saving dict for global step 10: accuracy = 0.4609375, average_loss = 1.7744806, global_step = 10, loss = 227.13351 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmpt0r_lxj6/model.ckpt-10 INFO:tensorflow:Loss for final step: 97.6104. ({'accuracy': 0.4609375, 'average_loss': 1.7744806, 'loss': 227.13351, 'global_step': 10}, [])
%tensorboard --logdir {classifier.model_dir}
TensorFlow 2: TensorBoard with a Keras callback and Model.fit
In this TensorFlow 2 example, you create and store logs with the tf.keras.callbacks.TensorBoard
callback, and train the model. The callback tracks the accuracy and loss per epoch. It is passed to Model.fit
in the callbacks
list.
%reload_ext tensorboard
def create_model():
return tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28), name='layers_flatten'),
tf.keras.layers.Dense(512, activation='relu', name='layers_dense'),
tf.keras.layers.Dropout(0.2, name='layers_dropout'),
tf.keras.layers.Dense(10, activation='softmax', name='layers_dense_2')
])
model = create_model()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'],
steps_per_execution=10)
log_dir = tempfile.mkdtemp()
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_dir,
histogram_freq=1) # Enable histogram computation with each epoch.
model.fit(x=x_train,
y=y_train,
epochs=10,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])
Epoch 1/10 1875/1875 [==============================] - 4s 2ms/step - loss: 0.2207 - accuracy: 0.9344 - val_loss: 0.0998 - val_accuracy: 0.9685 Epoch 2/10 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0966 - accuracy: 0.9692 - val_loss: 0.0787 - val_accuracy: 0.9759 Epoch 3/10 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0685 - accuracy: 0.9787 - val_loss: 0.0722 - val_accuracy: 0.9785 Epoch 4/10 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0537 - accuracy: 0.9830 - val_loss: 0.0710 - val_accuracy: 0.9773 Epoch 5/10 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0434 - accuracy: 0.9861 - val_loss: 0.0691 - val_accuracy: 0.9790 Epoch 6/10 1875/1875 [==============================] - 3s 1ms/step - loss: 0.0357 - accuracy: 0.9884 - val_loss: 0.0733 - val_accuracy: 0.9790 Epoch 7/10 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0310 - accuracy: 0.9897 - val_loss: 0.0688 - val_accuracy: 0.9808 Epoch 8/10 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0300 - accuracy: 0.9899 - val_loss: 0.0840 - val_accuracy: 0.9780 Epoch 9/10 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0258 - accuracy: 0.9913 - val_loss: 0.0754 - val_accuracy: 0.9793 Epoch 10/10 1875/1875 [==============================] - 2s 1ms/step - loss: 0.0225 - accuracy: 0.9924 - val_loss: 0.0715 - val_accuracy: 0.9827 <keras.callbacks.History at 0x7f251c2d2fa0>
%tensorboard --logdir {tensorboard_callback.log_dir}
Next steps
- Learn more about TensorBoard in the Get started guide.
- For lower level APIs, refer to the tf.summary migration to TensorFlow 2 guide.