diff --git a/01.fit_a_line/README.cn.md b/01.fit_a_line/README.cn.md index 428522cb51df8e50dd49f5c0a3baabd7182ab720..8d1a8c761e090f8c07a440b45ebe01301064ce69 100644 --- a/01.fit_a_line/README.cn.md +++ b/01.fit_a_line/README.cn.md @@ -196,28 +196,24 @@ feed_order=['x', 'y'] # Specify the directory to save the parameters params_dirname = "fit_a_line.inference.model" -# Plot data -from paddle.utils import Ploter train_title = "Train cost" test_title = "Test cost" -plot_cost = Ploter(train_title, test_title) step = 0 # event_handler prints training and testing info -def event_handler_plot(event): +def event_handler(event): global step if isinstance(event, EndStepEvent): if step % 10 == 0: # record a train cost every 10 batches - plot_cost.append(train_title, step, event.metrics[0]) + print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0])) if step % 100 == 0: # record a test cost every 100 batches test_metrics = trainer.test( reader=test_reader, feed_order=feed_order) - plot_cost.append(test_title, step, test_metrics[0]) - plot_cost.plot() + print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0])) if test_metrics[0] < 10.0: # If the accuracy is good enough, we can stop the training. @@ -242,7 +238,7 @@ def event_handler_plot(event): trainer.train( reader=train_reader, num_epochs=100, - event_handler=event_handler_plot, + event_handler=event_handler, feed_order=feed_order) ``` diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md index e6a1c847a775e005d7d16f179c67961304f1e8ff..13eef12b2b58316a87a407d4f1b6d3474f255c02 100644 --- a/01.fit_a_line/README.md +++ b/01.fit_a_line/README.md @@ -212,28 +212,24 @@ Moreover, an event handler is provided to print the training progress: # Specify the directory to save the parameters params_dirname = "fit_a_line.inference.model" -# Plot data -from paddle.utils import Ploter train_title = "Train cost" test_title = "Test cost" -plot_cost = Ploter(train_title, test_title) step = 0 # event_handler prints training and testing info -def event_handler_plot(event): +def event_handler(event): global step if isinstance(event, EndStepEvent): if step % 10 == 0: # record a train cost every 10 batches - plot_cost.append(train_title, step, event.metrics[0]) + print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0])) + if step % 100 == 0: # record a test cost every 100 batches test_metrics = trainer.test( reader=test_reader, feed_order=feed_order) - plot_cost.append(test_title, step, test_metrics[0]) - plot_cost.plot() - if test_metrics[0] < 10.0: print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0])) + if test_metrics[0] < 10.0: # If the accuracy is good enough, we can stop the training. print('loss is less than 10.0, stop') trainer.stop() @@ -258,7 +254,7 @@ We now can start training by calling `trainer.train()`. trainer.train( reader=train_reader, num_epochs=100, - event_handler=event_handler_plot, + event_handler=event_handler, feed_order=feed_order) ``` diff --git a/01.fit_a_line/index.cn.html b/01.fit_a_line/index.cn.html index 138021ad415eff07a2f3b6a440a31a35e1d0ad5e..e549995fe229b8e8850c85fd227cf9a91b0db224 100644 --- a/01.fit_a_line/index.cn.html +++ b/01.fit_a_line/index.cn.html @@ -238,28 +238,24 @@ feed_order=['x', 'y'] # Specify the directory to save the parameters params_dirname = "fit_a_line.inference.model" -# Plot data -from paddle.utils import Ploter train_title = "Train cost" test_title = "Test cost" -plot_cost = Ploter(train_title, test_title) step = 0 # event_handler prints training and testing info -def event_handler_plot(event): +def event_handler(event): global step if isinstance(event, EndStepEvent): if step % 10 == 0: # record a train cost every 10 batches - plot_cost.append(train_title, step, event.metrics[0]) + print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0])) if step % 100 == 0: # record a test cost every 100 batches test_metrics = trainer.test( reader=test_reader, feed_order=feed_order) - plot_cost.append(test_title, step, test_metrics[0]) - plot_cost.plot() + print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0])) if test_metrics[0] < 10.0: # If the accuracy is good enough, we can stop the training. @@ -284,7 +280,7 @@ def event_handler_plot(event): trainer.train( reader=train_reader, num_epochs=100, - event_handler=event_handler_plot, + event_handler=event_handler, feed_order=feed_order) ``` diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html index 7593fe93d1a8b8a2d831f2c46182e37ec502625a..744cbba8ff5e11ec33dd88013987587a96979394 100644 --- a/01.fit_a_line/index.html +++ b/01.fit_a_line/index.html @@ -254,28 +254,24 @@ Moreover, an event handler is provided to print the training progress: # Specify the directory to save the parameters params_dirname = "fit_a_line.inference.model" -# Plot data -from paddle.utils import Ploter train_title = "Train cost" test_title = "Test cost" -plot_cost = Ploter(train_title, test_title) step = 0 # event_handler prints training and testing info -def event_handler_plot(event): +def event_handler(event): global step if isinstance(event, EndStepEvent): if step % 10 == 0: # record a train cost every 10 batches - plot_cost.append(train_title, step, event.metrics[0]) + print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0])) + if step % 100 == 0: # record a test cost every 100 batches test_metrics = trainer.test( reader=test_reader, feed_order=feed_order) - plot_cost.append(test_title, step, test_metrics[0]) - plot_cost.plot() - if test_metrics[0] < 10.0: print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0])) + if test_metrics[0] < 10.0: # If the accuracy is good enough, we can stop the training. print('loss is less than 10.0, stop') trainer.stop() @@ -300,7 +296,7 @@ We now can start training by calling `trainer.train()`. trainer.train( reader=train_reader, num_epochs=100, - event_handler=event_handler_plot, + event_handler=event_handler, feed_order=feed_order) ``` diff --git a/01.fit_a_line/train.py b/01.fit_a_line/train.py index 5f99b0f7bd455b9dd5b719392ed03d4478c3a934..6077a16b2b663d020035597d1d3b0276238d9e30 100644 --- a/01.fit_a_line/train.py +++ b/01.fit_a_line/train.py @@ -69,26 +69,23 @@ feed_order = ['x', 'y'] # Specify the directory to save the parameters params_dirname = "fit_a_line.inference.model" -from paddle.utils import Ploter - train_title = "Train cost" test_title = "Test cost" -plot_cost = Ploter(train_title, test_title) step = 0 # event_handler prints training and testing info -def event_handler_plot(event): +def event_handler(event): global step if isinstance(event, EndStepEvent): if step % 10 == 0: # record a train cost every 10 batches - plot_cost.append(train_title, step, event.metrics[0]) + print("%s, Step %d, Cost %f" % + (train_title, step, event.metrics[0])) if step % 100 == 0: # record a test cost every 100 batches test_metrics = trainer.test( reader=test_reader, feed_order=feed_order) - plot_cost.append(test_title, step, test_metrics[0]) - plot_cost.plot() + print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0])) if test_metrics[0] < 10.0: # If the accuracy is good enough, we can stop the training. print('loss is less than 10.0, stop') @@ -106,7 +103,7 @@ def event_handler_plot(event): trainer.train( reader=train_reader, num_epochs=100, - event_handler=event_handler_plot, + event_handler=event_handler, feed_order=feed_order) diff --git a/02.recognize_digits/README.cn.md b/02.recognize_digits/README.cn.md index c21a3fa5386b478a778212844ca607938afafd11..144ecbbd38b03a2389ab68bb6707e7ea8cc64b8b 100644 --- a/02.recognize_digits/README.cn.md +++ b/02.recognize_digits/README.cn.md @@ -321,42 +321,6 @@ def event_handler(event): lists.append((event.epoch, avg_cost, acc)) ``` -`event_handler_plot` 可以用来在训练过程中画图如下： - -
-
-图7 训练结果 -
- - -```python -from paddle.utils import Ploter - -train_title = "Train cost" -test_title = "Test cost" -cost_ploter = Ploter(train_title, test_title) -step = 0 -lists = [] - -# event_handler to plot a figure -def event_handler_plot(event): - global step - if isinstance(event, EndStepEvent): - if step % 100 == 0: - # event.metrics maps with train program return arguments. - # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example. - cost_ploter.append(train_title, step, event.metrics[0]) - cost_ploter.plot() - step += 1 - if isinstance(event, EndEpochEvent): - # save parameters - trainer.save_params(params_dirname) - - avg_cost, acc = trainer.test( - reader=test_reader, feed_order=['img', 'label']) - cost_ploter.append(test_title, step, avg_cost) - lists.append((event.epoch, avg_cost, acc)) -``` #### 开始训练 diff --git a/02.recognize_digits/README.md b/02.recognize_digits/README.md index b32ba920cfde25ee349f78f3dc14d59240f624a9..44230810dd6ec199c1d937e48f096e59fb3dba52 100644 --- a/02.recognize_digits/README.md +++ b/02.recognize_digits/README.md @@ -324,38 +324,6 @@ def event_handler(event): lists.append((event.epoch, avg_cost, acc)) ``` -`event_handler_plot` is used to plot a figure like below： - -![png](./image/train_and_test.png) - -```python -from paddle.utils import Ploter - -train_title = "Train cost" -test_title = "Test cost" -cost_ploter = Ploter(train_title, test_title) -step = 0 -lists = [] - -# event_handler to plot a figure -def event_handler_plot(event): - global step - if isinstance(event, EndStepEvent): - if step % 100 == 0: - # event.metrics maps with train program return arguments. - # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example. - cost_ploter.append(train_title, step, event.metrics[0]) - cost_ploter.plot() - step += 1 - if isinstance(event, EndEpochEvent): - # save parameters - trainer.save_params(params_dirname) - - avg_cost, acc = trainer.test( - reader=test_reader, feed_order=['img', 'label']) - cost_ploter.append(test_title, step, avg_cost) - lists.append((event.epoch, avg_cost, acc)) -``` #### Start training @@ -365,7 +333,7 @@ Now that we setup the event_handler and the reader, we can start training the mo # Train the model now trainer.train( num_epochs=5, - event_handler=event_handler_plot, + event_handler=event_handler, reader=train_reader, feed_order=['img', 'label']) ``` diff --git a/02.recognize_digits/index.cn.html b/02.recognize_digits/index.cn.html index 55017f73573a1e236173377b14f03d4d7b567238..3b7df167753da39db84c0dfcb97a604ebe74dffa 100644 --- a/02.recognize_digits/index.cn.html +++ b/02.recognize_digits/index.cn.html @@ -363,42 +363,6 @@ def event_handler(event): lists.append((event.epoch, avg_cost, acc)) ``` -`event_handler_plot` 可以用来在训练过程中画图如下： - -
-
-图7 训练结果 -
- - -```python -from paddle.utils import Ploter - -train_title = "Train cost" -test_title = "Test cost" -cost_ploter = Ploter(train_title, test_title) -step = 0 -lists = [] - -# event_handler to plot a figure -def event_handler_plot(event): - global step - if isinstance(event, EndStepEvent): - if step % 100 == 0: - # event.metrics maps with train program return arguments. - # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example. - cost_ploter.append(train_title, step, event.metrics[0]) - cost_ploter.plot() - step += 1 - if isinstance(event, EndEpochEvent): - # save parameters - trainer.save_params(params_dirname) - - avg_cost, acc = trainer.test( - reader=test_reader, feed_order=['img', 'label']) - cost_ploter.append(test_title, step, avg_cost) - lists.append((event.epoch, avg_cost, acc)) -``` #### 开始训练 diff --git a/02.recognize_digits/index.html b/02.recognize_digits/index.html index 21a55a285e90520d3ac8593854f36a25c2d5fd46..8121a46f8b26f358b9f2fe898bc3603b1ae6d90f 100644 --- a/02.recognize_digits/index.html +++ b/02.recognize_digits/index.html @@ -366,38 +366,6 @@ def event_handler(event): lists.append((event.epoch, avg_cost, acc)) ``` -`event_handler_plot` is used to plot a figure like below： - -![png](./image/train_and_test.png) - -```python -from paddle.utils import Ploter - -train_title = "Train cost" -test_title = "Test cost" -cost_ploter = Ploter(train_title, test_title) -step = 0 -lists = [] - -# event_handler to plot a figure -def event_handler_plot(event): - global step - if isinstance(event, EndStepEvent): - if step % 100 == 0: - # event.metrics maps with train program return arguments. - # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example. - cost_ploter.append(train_title, step, event.metrics[0]) - cost_ploter.plot() - step += 1 - if isinstance(event, EndEpochEvent): - # save parameters - trainer.save_params(params_dirname) - - avg_cost, acc = trainer.test( - reader=test_reader, feed_order=['img', 'label']) - cost_ploter.append(test_title, step, avg_cost) - lists.append((event.epoch, avg_cost, acc)) -``` #### Start training @@ -407,7 +375,7 @@ Now that we setup the event_handler and the reader, we can start training the mo # Train the model now trainer.train( num_epochs=5, - event_handler=event_handler_plot, + event_handler=event_handler, reader=train_reader, feed_order=['img', 'label']) ``` diff --git a/03.image_classification/README.cn.md b/03.image_classification/README.cn.md index 3652ed6e6241e3725ee0731c23fc4f62cc56d586..761eed3c4347ec1eda0df1688ab7f0c45e31abba 100644 --- a/03.image_classification/README.cn.md +++ b/03.image_classification/README.cn.md @@ -383,42 +383,6 @@ test_reader = paddle.batch( 可以使用`event_handler`回调函数来观察训练过程，或进行测试等, 该回调函数是`trainer.train`函数里设定。 -`event_handler_plot`可以用来利用回调数据来打点画图: - -

-
-图12. 训练结果 -

- - -```python -params_dirname = "image_classification_resnet.inference.model" - -from paddle.utils import Ploter - -train_title = "Train cost" -test_title = "Test cost" -cost_ploter = Ploter(train_title, test_title) - -step = 0 -def event_handler_plot(event): - global step - if isinstance(event, EndStepEvent): - if step % 1 == 0: - cost_ploter.append(train_title, step, event.metrics[0]) - cost_ploter.plot() - step += 1 - if isinstance(event, EndEpochEvent): - avg_cost, accuracy = trainer.test( - reader=test_reader, - feed_order=['pixel', 'label']) - cost_ploter.append(test_title, step, avg_cost) - - # save parameters - if params_dirname is not None: - trainer.save_params(params_dirname) -``` - `event_handler` 用来在训练过程中输出文本日志 ```python diff --git a/03.image_classification/README.md b/03.image_classification/README.md index fc58147ab77bdd6f470df504f65847bd9363e9b6..73cfe690955bf001757e79e3e18eb62b45f0df86 100644 --- a/03.image_classification/README.md +++ b/03.image_classification/README.md @@ -385,38 +385,6 @@ test_reader = paddle.batch( Callback function `event_handler` will be called during training when a pre-defined event happens. -`event_handler_plot`is used to plot a figure like below： - -![png](./image/train_and_test.png) - -```python -params_dirname = "image_classification_resnet.inference.model" - -from paddle.utils import Ploter - -train_title = "Train cost" -test_title = "Test cost" -cost_ploter = Ploter(train_title, test_title) - -step = 0 -def event_handler_plot(event): - global step - if isinstance(event, EndStepEvent): - if step % 1 == 0: - cost_ploter.append(train_title, step, event.metrics[0]) - cost_ploter.plot() - step += 1 - if isinstance(event, EndEpochEvent): - avg_cost, accuracy = trainer.test( - reader=test_reader, - feed_order=['pixel', 'label']) - cost_ploter.append(test_title, step, avg_cost) - - # save parameters - if params_dirname is not None: - trainer.save_params(params_dirname) -``` - `event_handler` is used to plot some text data when training. ```python diff --git a/03.image_classification/index.cn.html b/03.image_classification/index.cn.html index 6278edb200f80f3336a2b4722756142bb708079e..da5aab5488df8018a22e652f59874e0301c9ad47 100644 --- a/03.image_classification/index.cn.html +++ b/03.image_classification/index.cn.html @@ -425,42 +425,6 @@ test_reader = paddle.batch( 可以使用`event_handler`回调函数来观察训练过程，或进行测试等, 该回调函数是`trainer.train`函数里设定。 -`event_handler_plot`可以用来利用回调数据来打点画图: - -

-
-图12. 训练结果 -

- - -```python -params_dirname = "image_classification_resnet.inference.model" - -from paddle.utils import Ploter - -train_title = "Train cost" -test_title = "Test cost" -cost_ploter = Ploter(train_title, test_title) - -step = 0 -def event_handler_plot(event): - global step - if isinstance(event, EndStepEvent): - if step % 1 == 0: - cost_ploter.append(train_title, step, event.metrics[0]) - cost_ploter.plot() - step += 1 - if isinstance(event, EndEpochEvent): - avg_cost, accuracy = trainer.test( - reader=test_reader, - feed_order=['pixel', 'label']) - cost_ploter.append(test_title, step, avg_cost) - - # save parameters - if params_dirname is not None: - trainer.save_params(params_dirname) -``` - `event_handler` 用来在训练过程中输出文本日志 ```python diff --git a/03.image_classification/index.html b/03.image_classification/index.html index fe6d7b92033443127b64371374dbe1ae8c7207d6..71bde172262a77e04d770e330670385fd74dadf2 100644 --- a/03.image_classification/index.html +++ b/03.image_classification/index.html @@ -427,38 +427,6 @@ test_reader = paddle.batch( Callback function `event_handler` will be called during training when a pre-defined event happens. -`event_handler_plot`is used to plot a figure like below： - -![png](./image/train_and_test.png) - -```python -params_dirname = "image_classification_resnet.inference.model" - -from paddle.utils import Ploter - -train_title = "Train cost" -test_title = "Test cost" -cost_ploter = Ploter(train_title, test_title) - -step = 0 -def event_handler_plot(event): - global step - if isinstance(event, EndStepEvent): - if step % 1 == 0: - cost_ploter.append(train_title, step, event.metrics[0]) - cost_ploter.plot() - step += 1 - if isinstance(event, EndEpochEvent): - avg_cost, accuracy = trainer.test( - reader=test_reader, - feed_order=['pixel', 'label']) - cost_ploter.append(test_title, step, avg_cost) - - # save parameters - if params_dirname is not None: - trainer.save_params(params_dirname) -``` - `event_handler` is used to plot some text data when training. ```python diff --git a/05.recommender_system/README.cn.md b/05.recommender_system/README.cn.md index ccd7286ab31cf06960c1753927e8f27cbc55e45e..0ea8bcca3b3cb68dc9c4bfbbab0c85c3b9c7eef5 100644 --- a/05.recommender_system/README.cn.md +++ b/05.recommender_system/README.cn.md @@ -439,30 +439,26 @@ feed_order = [ ```python # Specify the directory path to save the parameters params_dirname = "recommender_system.inference.model" - -from paddle.utils import Ploter -test_title = "Test cost" -plot_cost = Ploter(test_title) - - def event_handler(event): if isinstance(event, EndStepEvent): + test_reader = paddle.batch( + paddle.dataset.movielens.test(), batch_size=BATCH_SIZE) avg_cost_set = trainer.test( reader=test_reader, feed_order=feed_order) # get avg cost avg_cost = np.array(avg_cost_set).mean() - plot_cost.append(test_title, event.step, avg_cost_set[0]) - plot_cost.plot() - print("avg_cost: %s" % avg_cost) - print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, - float(avg_cost))) - if event.step == 20: # Adjust this number for accuracy + if float(avg_cost) < 4: # Change this number to adjust accuracy trainer.save_params(params_dirname) trainer.stop() + else: + print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, + float(avg_cost))) + if math.isnan(float(avg_cost)): + sys.exit("got NaN loss, training failed.") ``` ### 开始训练 diff --git a/05.recommender_system/README.md b/05.recommender_system/README.md index 5f3e395601de8cd41a1334b90880e456c8de076f..718709361beab54dc0730fa1ca531caaeb32d90e 100644 --- a/05.recommender_system/README.md +++ b/05.recommender_system/README.md @@ -415,30 +415,26 @@ For example, we can check the cost by `trainer.test` when `EndStepEvent` occurs # Specify the directory path to save the parameters params_dirname = "recommender_system.inference.model" -from paddle.utils import Ploter -test_title = "Test cost" -plot_cost = Ploter(test_title) - - def event_handler(event): if isinstance(event, EndStepEvent): + test_reader = paddle.batch( + paddle.dataset.movielens.test(), batch_size=BATCH_SIZE) avg_cost_set = trainer.test( reader=test_reader, feed_order=feed_order) # get avg cost avg_cost = np.array(avg_cost_set).mean() - plot_cost.append(test_title, event.step, avg_cost_set[0]) - plot_cost.plot() - print("avg_cost: %s" % avg_cost) - print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, - float(avg_cost))) - if event.step == 20: # Adjust this number for accuracy + if float(avg_cost) < 4: # Change this number to adjust accuracy trainer.save_params(params_dirname) trainer.stop() - + else: + print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, + float(avg_cost))) + if math.isnan(float(avg_cost)): + sys.exit("got NaN loss, training failed.") ``` ### Training diff --git a/05.recommender_system/index.cn.html b/05.recommender_system/index.cn.html index 3c9f63ffd4b9e0987deb6839b07f5555a6dbfd98..974725df2c86b1cd4a2e4bae0945b7cc382288fb 100644 --- a/05.recommender_system/index.cn.html +++ b/05.recommender_system/index.cn.html @@ -481,30 +481,26 @@ feed_order = [ ```python # Specify the directory path to save the parameters params_dirname = "recommender_system.inference.model" - -from paddle.utils import Ploter -test_title = "Test cost" -plot_cost = Ploter(test_title) - - def event_handler(event): if isinstance(event, EndStepEvent): + test_reader = paddle.batch( + paddle.dataset.movielens.test(), batch_size=BATCH_SIZE) avg_cost_set = trainer.test( reader=test_reader, feed_order=feed_order) # get avg cost avg_cost = np.array(avg_cost_set).mean() - plot_cost.append(test_title, event.step, avg_cost_set[0]) - plot_cost.plot() - print("avg_cost: %s" % avg_cost) - print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, - float(avg_cost))) - if event.step == 20: # Adjust this number for accuracy + if float(avg_cost) < 4: # Change this number to adjust accuracy trainer.save_params(params_dirname) trainer.stop() + else: + print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, + float(avg_cost))) + if math.isnan(float(avg_cost)): + sys.exit("got NaN loss, training failed.") ``` ### 开始训练 diff --git a/05.recommender_system/index.html b/05.recommender_system/index.html index 512f5f4779d5be914a3f80697b7646c8da85c351..403da9fc02348f2936e1ca6862660a9a5d624776 100644 --- a/05.recommender_system/index.html +++ b/05.recommender_system/index.html @@ -457,30 +457,26 @@ For example, we can check the cost by `trainer.test` when `EndStepEvent` occurs # Specify the directory path to save the parameters params_dirname = "recommender_system.inference.model" -from paddle.utils import Ploter -test_title = "Test cost" -plot_cost = Ploter(test_title) - - def event_handler(event): if isinstance(event, EndStepEvent): + test_reader = paddle.batch( + paddle.dataset.movielens.test(), batch_size=BATCH_SIZE) avg_cost_set = trainer.test( reader=test_reader, feed_order=feed_order) # get avg cost avg_cost = np.array(avg_cost_set).mean() - plot_cost.append(test_title, event.step, avg_cost_set[0]) - plot_cost.plot() - print("avg_cost: %s" % avg_cost) - print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, - float(avg_cost))) - if event.step == 20: # Adjust this number for accuracy + if float(avg_cost) < 4: # Change this number to adjust accuracy trainer.save_params(params_dirname) trainer.stop() - + else: + print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, + float(avg_cost))) + if math.isnan(float(avg_cost)): + sys.exit("got NaN loss, training failed.") ``` ### Training