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Update reproducibility_recipes.py #1941

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Oct 17, 2024
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Update reproducibility_recipes.py #1941

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6bf16bf
Update reproducibility_recipes.py
Frightera Oct 1, 2024
0130fa2
Fix formatting and add the generated files
Frightera Oct 17, 2024
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7 changes: 6 additions & 1 deletion examples/keras_recipes/ipynb/reproducibility_recipes.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -252,8 +252,13 @@
" )\n",
"\n",
" model.compile(\n",
" optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"]\n",
" optimizer=\"adam\",\n",
" loss=\"sparse_categorical_crossentropy\",\n",
" metrics=[\"accuracy\"],\n",
" jit_compile=False,\n",
" )\n",
" # jit_compile's default value is \"auto\" which will cause some problems in some\n",
" # ops, therefore it's set to False.\n",
"\n",
" # model.fit has a `shuffle` parameter which has a default value of `True`.\n",
" # If you are using array-like objects, this will shuffle the data before\n",
Expand Down
62 changes: 37 additions & 25 deletions examples/keras_recipes/md/reproducibility_recipes.md
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Original file line number Diff line number Diff line change
Expand Up @@ -87,89 +87,89 @@ for initializer in initializers_list:
<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.RandomNormal'>
Iteration --> 0 // Result --> 0.05609520897269249
Iteration --> 1 // Result --> 0.05609520897269249
Iteration --> 0 // Result --> 0.000790853810030967
Iteration --> 1 // Result --> 0.000790853810030967
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.RandomUniform'>
Iteration --> 0 // Result --> 0.03690483793616295
Iteration --> 1 // Result --> 0.03690483793616295
Iteration --> 0 // Result --> -0.02175668440759182
Iteration --> 1 // Result --> -0.02175668440759182
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.TruncatedNormal'>
Iteration --> 0 // Result --> 0.05230803042650223
Iteration --> 1 // Result --> 0.05230803042650223
Iteration --> 0 // Result --> 0.000790853810030967
Iteration --> 1 // Result --> 0.000790853810030967
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.VarianceScaling'>
Iteration --> 0 // Result --> 1.1893247365951538
Iteration --> 1 // Result --> 1.1893247365951538
Iteration --> 0 // Result --> 0.017981600016355515
Iteration --> 1 // Result --> 0.017981600016355515
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.GlorotNormal'>
Iteration --> 0 // Result --> 1.1893247365951538
Iteration --> 1 // Result --> 1.1893247365951538
Iteration --> 0 // Result --> 0.017981600016355515
Iteration --> 1 // Result --> 0.017981600016355515
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.GlorotUniform'>
Iteration --> 0 // Result --> 1.2784210443496704
Iteration --> 1 // Result --> 1.2784210443496704
Iteration --> 0 // Result --> -0.7536736726760864
Iteration --> 1 // Result --> -0.7536736726760864
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.HeNormal'>
Iteration --> 0 // Result --> 1.6819592714309692
Iteration --> 1 // Result --> 1.6819592714309692
Iteration --> 0 // Result --> 0.025429822504520416
Iteration --> 1 // Result --> 0.025429822504520416
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.HeUniform'>
Iteration --> 0 // Result --> 1.8079603910446167
Iteration --> 1 // Result --> 1.8079603910446167
Iteration --> 0 // Result --> -1.065855622291565
Iteration --> 1 // Result --> -1.065855622291565
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.LecunNormal'>
Iteration --> 0 // Result --> 1.1893247365951538
Iteration --> 1 // Result --> 1.1893247365951538
Iteration --> 0 // Result --> 0.017981600016355515
Iteration --> 1 // Result --> 0.017981600016355515
```
</div>


<div class="k-default-codeblock">
```
Running <class 'keras.src.initializers.random_initializers.LecunUniform'>
Iteration --> 0 // Result --> 1.2784210443496704
Iteration --> 1 // Result --> 1.2784210443496704
Iteration --> 0 // Result --> -0.7536736726760864
Iteration --> 1 // Result --> -0.7536736726760864
```
</div>

Expand Down Expand Up @@ -273,8 +273,13 @@ def train_model(train_data: tf.data.Dataset, test_data: tf.data.Dataset) -> dict
)

model.compile(
optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
jit_compile=False,
)
# jit_compile's default value is "auto" which will cause some problems in some
# ops, therefore it's set to False.

# model.fit has a `shuffle` parameter which has a default value of `True`.
# If you are using array-like objects, this will shuffle the data before
Expand All @@ -298,6 +303,13 @@ train_ds = tf.data.Dataset.from_tensor_slices((train_images, train_labels))
test_ds = tf.data.Dataset.from_tensor_slices((test_images, test_labels))
```

<div class="k-default-codeblock">
```
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step

```
</div>
Remember we called `tf.config.experimental.enable_op_determinism()` at the
beginning of the function. This makes the `tf.data` operations deterministic.
However, making `tf.data` operations deterministic comes with a performance
Expand Down Expand Up @@ -375,11 +387,11 @@ history = train_model(train_data, test_data)
<div class="k-default-codeblock">
```
Epoch 1/2
938/938 ━━━━━━━━━━━━━━━━━━━━ 26s 27ms/step - accuracy: 0.5418 - loss: 1.2867 - val_accuracy: 0.9291 - val_loss: 0.2303
938/938 ━━━━━━━━━━━━━━━━━━━━ 73s 73ms/step - accuracy: 0.5726 - loss: 1.2175 - val_accuracy: 0.9401 - val_loss: 0.1924
Epoch 2/2
938/938 ━━━━━━━━━━━━━━━━━━━━ 25s 26ms/step - accuracy: 0.9075 - loss: 0.2983 - val_accuracy: 0.9583 - val_loss: 0.1343
157/157 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - accuracy: 0.9512 - loss: 0.1559
Model accuracy on test data: 95.83%
938/938 ━━━━━━━━━━━━━━━━━━━━ 89s 81ms/step - accuracy: 0.9105 - loss: 0.2885 - val_accuracy: 0.9630 - val_loss: 0.1131
157/157 ━━━━━━━━━━━━━━━━━━━━ 3s 17ms/step - accuracy: 0.9553 - loss: 0.1353
Model accuracy on test data: 96.30%

```
</div>
Expand Down
7 changes: 6 additions & 1 deletion examples/keras_recipes/reproducibility_recipes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -151,8 +151,13 @@ def train_model(train_data: tf.data.Dataset, test_data: tf.data.Dataset) -> dict
)

model.compile(
optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
jit_compile=False,
)
# jit_compile's default value is "auto" which will cause some problems in some
# ops, therefore it's set to False.

# model.fit has a `shuffle` parameter which has a default value of `True`.
# If you are using array-like objects, this will shuffle the data before
Expand Down
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