Suppress training output warnings and remove intermediate epoch PNG visualizations in Focal Modulation Network tutorial

Author: LakshmiKalaKadali

examples/vision/focal_modulation_network.py

@@ -78,14 +78,26 @@
 import os
 
 # Set backend before importing keras
-os.environ["KERAS_BACKEND"] = "tensorflow"  # Or "torch" or "tensorflow"
+os.environ["KERAS_BACKEND"] = "tensorflow"  # or "torch" or "jax"
+# Suppress TensorFlow C++ logging (XLA messages)
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 import numpy as np
 import keras
 from keras import layers
 from keras import ops
 from matplotlib import pyplot as plt
 from random import randint
+import warnings
+import logging
+
+# Suppress warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", message=".*build.*method.*")
+warnings.filterwarnings("ignore", message=".*tf.function.*retracing.*")
+
+# Suppress TensorFlow logging
+logging.getLogger("tensorflow").setLevel(logging.ERROR)
 
 # Set seed for reproducibility using Keras 3 utility.
 keras.utils.set_random_seed(42)
@@ -837,7 +849,7 @@ def __call__(self, step):
     train_ds,
     validation_data=val_ds,
     epochs=EPOCHS,
-    callbacks=[TrainMonitor(epoch_interval=5)],
+    callbacks=[],
 )
 
 """

examples/vision/img/focal_modulation_network/focal_modulation_network_35_0.png

@@ -102,14 +102,26 @@
     "import os\n",
     "\n",
     "# Set backend before importing keras\n",
-    "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"  # Or \"torch\" or \"tensorflow\"\n",
+    "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"  # or \"torch\" or \"jax\"\n",
+    "# Suppress TensorFlow C++ logging (XLA messages)\n",
+    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n",
     "\n",
     "import numpy as np\n",
     "import keras\n",
     "from keras import layers\n",
     "from keras import ops\n",
     "from matplotlib import pyplot as plt\n",
     "from random import randint\n",
+    "import warnings\n",
+    "import logging\n",
+    "\n",
+    "# Suppress warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
+    "warnings.filterwarnings(\"ignore\", message=\".*build.*method.*\")\n",
+    "warnings.filterwarnings(\"ignore\", message=\".*tf.function.*retracing.*\")\n",
+    "\n",
+    "# Suppress TensorFlow logging\n",
+    "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\n",
     "\n",
     "# Set seed for reproducibility using Keras 3 utility.\n",
     "keras.utils.set_random_seed(42)"
@@ -1059,7 +1071,7 @@
     "    train_ds,\n",
     "    validation_data=val_ds,\n",
     "    epochs=EPOCHS,\n",
-    "    callbacks=[TrainMonitor(epoch_interval=5)],\n",
+    "    callbacks=[],\n",
     ")"
    ]
   },

examples/vision/img/focal_modulation_network/focal_modulation_network_37_0.png

@@ -80,14 +80,26 @@ Keras 3 allows this model to run on JAX, PyTorch, or TensorFlow. We use keras.op
 import os
 
 # Set backend before importing keras
-os.environ["KERAS_BACKEND"] = "tensorflow"  # Or "torch" or "tensorflow"
+os.environ["KERAS_BACKEND"] = "tensorflow"  # or "torch" or "jax"
+# Suppress TensorFlow C++ logging (XLA messages)
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 import numpy as np
 import keras
 from keras import layers
 from keras import ops
 from matplotlib import pyplot as plt
 from random import randint
+import warnings
+import logging
+
+# Suppress warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+warnings.filterwarnings("ignore", message=".*build.*method.*")
+warnings.filterwarnings("ignore", message=".*tf.function.*retracing.*")
+
+# Suppress TensorFlow logging
+logging.getLogger("tensorflow").setLevel(logging.ERROR)
 
 # Set seed for reproducibility using Keras 3 utility.
 keras.utils.set_random_seed(42)
@@ -847,161 +859,96 @@ history = model.fit(
     train_ds,
     validation_data=val_ds,
     epochs=EPOCHS,
-    callbacks=[TrainMonitor(epoch_interval=5)],
+    callbacks=[],
 )
 ```
 
 <div class="k-default-codeblock">
 ```
 Epoch 1/20
 
-/Users/lakshmikala/node2vec_env/lib/python3.12/site-packages/keras/src/layers/layer.py:424: UserWarning: `build()` was called on layer 'focal_modulation_block', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
-  warnings.warn(
-/Users/lakshmikala/node2vec_env/lib/python3.12/site-packages/keras/src/layers/layer.py:424: UserWarning: `build()` was called on layer 'focal_modulation_block_1', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
-  warnings.warn(
-
-/Users/lakshmikala/node2vec_env/lib/python3.12/site-packages/keras/src/layers/layer.py:424: UserWarning: `build()` was called on layer 'focal_modulation_block_2', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
-  warnings.warn(
-/Users/lakshmikala/node2vec_env/lib/python3.12/site-packages/keras/src/layers/layer.py:424: UserWarning: `build()` was called on layer 'focal_modulation_block_3', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
-  warnings.warn(
-
-/Users/lakshmikala/node2vec_env/lib/python3.12/site-packages/keras/src/layers/layer.py:424: UserWarning: `build()` was called on layer 'focal_modulation_block_4', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
-  warnings.warn(
-
-/Users/lakshmikala/node2vec_env/lib/python3.12/site-packages/keras/src/layers/layer.py:424: UserWarning: `build()` was called on layer 'focal_modulation_block_5', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
-  warnings.warn(
-/Users/lakshmikala/node2vec_env/lib/python3.12/site-packages/keras/src/layers/layer.py:424: UserWarning: `build()` was called on layer 'focal_modulation_block_6', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
-  warnings.warn(
-
-/Users/lakshmikala/node2vec_env/lib/python3.12/site-packages/keras/src/layers/layer.py:424: UserWarning: `build()` was called on layer 'focal_modulation_network', however the layer does not have a `build()` method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a proper `build()` method.
-  warnings.warn(
-
 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
-I0000 00:00:1770700186.220793 2002752 service.cc:152] XLA service 0x16cf639d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
-I0000 00:00:1770700186.220808 2002752 service.cc:160]   StreamExecutor device (0): Host, Default Version
-I0000 00:00:1770700186.251643 2002752 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
+I0000 00:00:1772431619.408199 4985427 service.cc:152] XLA service 0x166d7d250 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
+I0000 00:00:1772431619.408213 4985427 service.cc:160]   StreamExecutor device (0): Host, Default Version
+I0000 00:00:1772431619.566351 4985427 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 312s 964ms/step - accuracy: 0.1826 - loss: 2.1990 - val_accuracy: 0.2426 - val_loss: 2.0434
+313/313 ━━━━━━━━━━━━━━━━━━━━ 310s 958ms/step - accuracy: 0.1826 - loss: 2.1990 - val_accuracy: 0.2426 - val_loss: 2.0434
 
 Epoch 2/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 302s 964ms/step - accuracy: 0.2891 - loss: 1.8906 - val_accuracy: 0.3191 - val_loss: 1.8333
+313/313 ━━━━━━━━━━━━━━━━━━━━ 301s 963ms/step - accuracy: 0.2891 - loss: 1.8906 - val_accuracy: 0.3191 - val_loss: 1.8333
 
 Epoch 3/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 303s 968ms/step - accuracy: 0.3669 - loss: 1.7095 - val_accuracy: 0.3869 - val_loss: 1.6693
+313/313 ━━━━━━━━━━━━━━━━━━━━ 311s 994ms/step - accuracy: 0.3669 - loss: 1.7095 - val_accuracy: 0.3869 - val_loss: 1.6693
 
 Epoch 4/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 308s 984ms/step - accuracy: 0.4221 - loss: 1.5685 - val_accuracy: 0.4188 - val_loss: 1.5894
+313/313 ━━━━━━━━━━━━━━━━━━━━ 305s 975ms/step - accuracy: 0.4221 - loss: 1.5685 - val_accuracy: 0.4188 - val_loss: 1.5894
 
 Epoch 5/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 905ms/step - accuracy: 0.4501 - loss: 1.5031
-
-WARNING:tensorflow:5 out of the last 5 calls to <function conv.<locals>._conv_xla at 0x3190abc40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
-
-WARNING:tensorflow:6 out of the last 6 calls to <function conv.<locals>._conv_xla at 0x3190abd80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
-```
-</div>
-
-![png](/img/examples/vision/focal_modulation_network/focal_modulation_network_33_1582.png)
-    
-
-
-<div class="k-default-codeblock">
-```
-313/313 ━━━━━━━━━━━━━━━━━━━━ 313s 1s/step - accuracy: 0.4618 - loss: 1.4759 - val_accuracy: 0.4519 - val_loss: 1.5107
+313/313 ━━━━━━━━━━━━━━━━━━━━ 301s 961ms/step - accuracy: 0.4618 - loss: 1.4759 - val_accuracy: 0.4519 - val_loss: 1.5107
 
 Epoch 6/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 316s 1s/step - accuracy: 0.4919 - loss: 1.4076 - val_accuracy: 0.4692 - val_loss: 1.4941
+313/313 ━━━━━━━━━━━━━━━━━━━━ 308s 984ms/step - accuracy: 0.4943 - loss: 1.3977 - val_accuracy: 0.4743 - val_loss: 1.5007
 
 Epoch 7/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 312s 997ms/step - accuracy: 0.5189 - loss: 1.3461 - val_accuracy: 0.5032 - val_loss: 1.3940
+313/313 ━━━━━━━━━━━━━━━━━━━━ 304s 971ms/step - accuracy: 0.5185 - loss: 1.3445 - val_accuracy: 0.5042 - val_loss: 1.3920
 
 Epoch 8/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 307s 981ms/step - accuracy: 0.5356 - loss: 1.3025 - val_accuracy: 0.5182 - val_loss: 1.3580
+313/313 ━━━━━━━━━━━━━━━━━━━━ 308s 985ms/step - accuracy: 0.5350 - loss: 1.3001 - val_accuracy: 0.5192 - val_loss: 1.3544
 
 Epoch 9/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 299s 954ms/step - accuracy: 0.5440 - loss: 1.2654 - val_accuracy: 0.5273 - val_loss: 1.3291
+313/313 ━━━━━━━━━━━━━━━━━━━━ 308s 984ms/step - accuracy: 0.5451 - loss: 1.2637 - val_accuracy: 0.5247 - val_loss: 1.3289
 
 Epoch 10/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 866ms/step - accuracy: 0.5588 - loss: 1.2346
-```
-</div>
-
-![png](/img/examples/vision/focal_modulation_network/focal_modulation_network_33_3158.png)
-    
-
-
-<div class="k-default-codeblock">
-```
-313/313 ━━━━━━━━━━━━━━━━━━━━ 301s 961ms/step - accuracy: 0.5600 - loss: 1.2305 - val_accuracy: 0.5273 - val_loss: 1.3158
+313/313 ━━━━━━━━━━━━━━━━━━━━ 306s 976ms/step - accuracy: 0.5607 - loss: 1.2300 - val_accuracy: 0.5312 - val_loss: 1.3112
 
 Epoch 11/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 302s 965ms/step - accuracy: 0.5741 - loss: 1.1958 - val_accuracy: 0.5248 - val_loss: 1.3298
+313/313 ━━━━━━━━━━━━━━━━━━━━ 299s 956ms/step - accuracy: 0.5730 - loss: 1.1985 - val_accuracy: 0.5354 - val_loss: 1.3262
 
 Epoch 12/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 302s 965ms/step - accuracy: 0.5836 - loss: 1.1713 - val_accuracy: 0.5500 - val_loss: 1.2602
+313/313 ━━━━━━━━━━━━━━━━━━━━ 304s 970ms/step - accuracy: 0.5802 - loss: 1.1739 - val_accuracy: 0.5484 - val_loss: 1.2850
 
 Epoch 13/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 297s 947ms/step - accuracy: 0.5900 - loss: 1.1483 - val_accuracy: 0.5626 - val_loss: 1.2348
+313/313 ━━━━━━━━━━━━━━━━━━━━ 312s 996ms/step - accuracy: 0.5905 - loss: 1.1494 - val_accuracy: 0.5640 - val_loss: 1.2334
 
 Epoch 14/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 304s 970ms/step - accuracy: 0.5987 - loss: 1.1270 - val_accuracy: 0.5657 - val_loss: 1.2249
+313/313 ━━━━━━━━━━━━━━━━━━━━ 307s 982ms/step - accuracy: 0.5973 - loss: 1.1309 - val_accuracy: 0.5641 - val_loss: 1.2307
 
 Epoch 15/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 884ms/step - accuracy: 0.6118 - loss: 1.1106
-```
-</div>
-
-![png](/img/examples/vision/focal_modulation_network/focal_modulation_network_33_4734.png)
-    
-
-
-<div class="k-default-codeblock">
-```
-313/313 ━━━━━━━━━━━━━━━━━━━━ 308s 982ms/step - accuracy: 0.6081 - loss: 1.1134 - val_accuracy: 0.5671 - val_loss: 1.2246
+313/313 ━━━━━━━━━━━━━━━━━━━━ 313s 999ms/step - accuracy: 0.6072 - loss: 1.1127 - val_accuracy: 0.5740 - val_loss: 1.2129
 
 Epoch 16/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 298s 954ms/step - accuracy: 0.6105 - loss: 1.0981 - val_accuracy: 0.5708 - val_loss: 1.2035
+313/313 ━━━━━━━━━━━━━━━━━━━━ 311s 992ms/step - accuracy: 0.6120 - loss: 1.0956 - val_accuracy: 0.5734 - val_loss: 1.2059
 
 Epoch 17/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 302s 964ms/step - accuracy: 0.6144 - loss: 1.0838 - val_accuracy: 0.5770 - val_loss: 1.2002
+313/313 ━━━━━━━━━━━━━━━━━━━━ 5871s 19s/step - accuracy: 0.6145 - loss: 1.0874 - val_accuracy: 0.5788 - val_loss: 1.1996
 
 Epoch 18/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 308s 984ms/step - accuracy: 0.6209 - loss: 1.0799 - val_accuracy: 0.5764 - val_loss: 1.1978
+313/313 ━━━━━━━━━━━━━━━━━━━━ 300s 959ms/step - accuracy: 0.6164 - loss: 1.0800 - val_accuracy: 0.5797 - val_loss: 1.1991
 
 Epoch 19/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 315s 1s/step - accuracy: 0.6174 - loss: 1.0772 - val_accuracy: 0.5777 - val_loss: 1.1951
+313/313 ━━━━━━━━━━━━━━━━━━━━ 297s 950ms/step - accuracy: 0.6212 - loss: 1.0737 - val_accuracy: 0.5828 - val_loss: 1.1924
 
 Epoch 20/20
 
-313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 896ms/step - accuracy: 0.6249 - loss: 1.0723
-```
-</div>
-
-![png](/img/examples/vision/focal_modulation_network/focal_modulation_network_33_6310.png)
-    
-
-
-<div class="k-default-codeblock">
-```
-313/313 ━━━━━━━━━━━━━━━━━━━━ 311s 993ms/step - accuracy: 0.6240 - loss: 1.0710 - val_accuracy: 0.5775 - val_loss: 1.1971
+313/313 ━━━━━━━━━━━━━━━━━━━━ 297s 950ms/step - accuracy: 0.6204 - loss: 1.0702 - val_accuracy: 0.5822 - val_loss: 1.1950
 ```
 </div>