diff --git a/examples/structured_data/classification_with_grn_and_vsn.py b/examples/structured_data/classification_with_grn_and_vsn.py index 49164a600a..39611755aa 100644 --- a/examples/structured_data/classification_with_grn_and_vsn.py +++ b/examples/structured_data/classification_with_grn_and_vsn.py @@ -182,20 +182,6 @@ valid_data.to_csv(valid_data_file, index=False, header=False) test_data.to_csv(test_data_file, index=False, header=False) -""" -Clean the directory for the downloaded files except the .tar.gz file and -also remove the empty directories -""" - -subprocess.run( - f'find {extracted_path} -type f ! -name "*.tar.gz" -exec rm -f {{}} +', - shell=True, - check=True, -) -subprocess.run( - f"find {extracted_path} -type d -empty -exec rmdir {{}} +", shell=True, check=True -) - """ ## Define dataset metadata @@ -337,6 +323,10 @@ def __init__(self, units): def call(self, inputs): return self.linear(inputs) * self.sigmoid(inputs) + # Remove build warnings + def build(self): + self.built = True + """ ## Implement the Gated Residual Network @@ -372,6 +362,10 @@ def call(self, inputs): x = self.layer_norm(x) return x + # Remove build warnings + def build(self): + self.built = True + """ ## Implement the Variable Selection Network @@ -446,52 +440,9 @@ def call(self, inputs): for idx, input in enumerate(concat_inputs): x.append(self.grns[idx](input)) x = keras.ops.stack(x, axis=1) - - # The reason for each individual backend calculation is that I couldn't find - # the equivalent keras operation that is backend-agnostic. In the following case there,s - # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul - # for all backends, but due to jax jit tracing it results in an error. - def matmul_dependent_on_backend(tensor_1, tensor_2): - """ - Function for executing matmul for each backend. - """ - # jax backend - if keras.backend.backend() == "jax": - import jax.numpy as jnp - - result = jnp.sum(tensor_1 * tensor_2, axis=1) - elif keras.backend.backend() == "torch": - result = torch.sum(tensor_1 * tensor_2, dim=1) - # tensorflow backend - elif keras.backend.backend() == "tensorflow": - result = keras.ops.squeeze(tf.matmul(tensor_1, tensor_2, transpose_a=True), axis=1) - # unsupported backend exception - else: - raise ValueError( - "Unsupported backend: {}".format(keras.backend.backend()) - ) - return result - - # jax backend - if keras.backend.backend() == "jax": - # This repetative imports are intentional to force the idea of backend - # separation - import jax.numpy as jnp - - result_jax = matmul_dependent_on_backend(v, x) - return result_jax - # torch backend - if keras.backend.backend() == "torch": - import torch - - result_torch = matmul_dependent_on_backend(v, x) - return result_torch - # tensorflow backend - if keras.backend.backend() == "tensorflow": - import tensorflow as tf - - result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) - return result_tf + return keras.ops.squeeze( + keras.ops.matmul(keras.ops.transpose(v, axes=[0, 2, 1]), x), axis=1 + ) # to remove the build warnings def build(self): @@ -520,7 +471,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 1 # maybe adjusted to a desired value +num_epochs = 1 # may be adjusted to a desired value encoding_size = 16 model = create_model(encoding_size) diff --git a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb index 1180954978..40aa463bb8 100644 --- a/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb +++ b/examples/structured_data/ipynb/classification_with_grn_and_vsn.ipynb @@ -282,34 +282,6 @@ "test_data.to_csv(test_data_file, index=False, header=False)" ] }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text" - }, - "source": [ - "Clean the directory for the downloaded files except the .tar.gz file and\n", - "also remove the empty directories" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab_type": "code" - }, - "outputs": [], - "source": [ - "subprocess.run(\n", - " f'find {extracted_path} -type f ! -name \"*.tar.gz\" -exec rm -f {{}} +',\n", - " shell=True,\n", - " check=True,\n", - ")\n", - "subprocess.run(\n", - " f\"find {extracted_path} -type d -empty -exec rmdir {{}} +\", shell=True, check=True\n", - ")" - ] - }, { "cell_type": "markdown", "metadata": { @@ -505,6 +477,10 @@ "\n", " def call(self, inputs):\n", " return self.linear(inputs) * self.sigmoid(inputs)\n", + "\n", + " # Remove build warnings\n", + " def build(self):\n", + " self.built = True\n", "" ] }, @@ -554,6 +530,10 @@ " x = inputs + self.gated_linear_unit(x)\n", " x = self.layer_norm(x)\n", " return x\n", + "\n", + " # Remove build warnings\n", + " def build(self):\n", + " self.built = True\n", "" ] }, @@ -642,52 +622,9 @@ " for idx, input in enumerate(concat_inputs):\n", " x.append(self.grns[idx](input))\n", " x = keras.ops.stack(x, axis=1)\n", - "\n", - " # The reason for each individual backend calculation is that I couldn't find\n", - " # the equivalent keras operation that is backend-agnostic. In the following case there,s\n", - " # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul\n", - " # for all backends, but due to jax jit tracing it results in an error.\n", - " def matmul_dependent_on_backend(thsi, v):\n", - " \"\"\"\n", - " Function for executing matmul for each backend.\n", - " \"\"\"\n", - " # jax backend\n", - " if keras.backend.backend() == \"jax\":\n", - " import jax.numpy as jnp\n", - "\n", - " result = jnp.sum(thsi * v, axis=1)\n", - " elif keras.backend.backend() == \"torch\":\n", - " result = torch.sum(thsi * v, dim=1)\n", - " # tensorflow backend\n", - " elif keras.backend.backend() == \"tensorflow\":\n", - " result = keras.ops.squeeze(tf.matmul(thsi, v, transpose_a=True), axis=1)\n", - " # unsupported backend exception\n", - " else:\n", - " raise ValueError(\n", - " \"Unsupported backend: {}\".format(keras.backend.backend())\n", - " )\n", - " return result\n", - "\n", - " # jax backend\n", - " if keras.backend.backend() == \"jax\":\n", - " # This repetative imports are intentional to force the idea of backend\n", - " # separation\n", - " import jax.numpy as jnp\n", - "\n", - " result_jax = matmul_dependent_on_backend(v, x)\n", - " return result_jax\n", - " # torch backend\n", - " if keras.backend.backend() == \"torch\":\n", - " import torch\n", - "\n", - " result_torch = matmul_dependent_on_backend(v, x)\n", - " return result_torch\n", - " # tensorflow backend\n", - " if keras.backend.backend() == \"tensorflow\":\n", - " import tensorflow as tf\n", - "\n", - " result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1)\n", - " return result_tf\n", + " return keras.ops.squeeze(\n", + " keras.ops.matmul(keras.ops.transpose(v, axes=[0, 2, 1]), x), axis=1\n", + " )\n", "\n", " # to remove the build warnings\n", " def build(self):\n", @@ -744,7 +681,7 @@ "learning_rate = 0.001\n", "dropout_rate = 0.15\n", "batch_size = 265\n", - "num_epochs = 1 # maybe adjusted to a desired value\n", + "num_epochs = 1 # may be adjusted to a desired value\n", "encoding_size = 16\n", "\n", "model = create_model(encoding_size)\n", diff --git a/examples/structured_data/md/classification_with_grn_and_vsn.md b/examples/structured_data/md/classification_with_grn_and_vsn.md index 602be61966..6bd5bd9539 100644 --- a/examples/structured_data/md/classification_with_grn_and_vsn.md +++ b/examples/structured_data/md/classification_with_grn_and_vsn.md @@ -206,30 +206,6 @@ valid_data.to_csv(valid_data_file, index=False, header=False) test_data.to_csv(test_data_file, index=False, header=False) ``` -Clean the directory for the downloaded files except the .tar.gz file and -also remove the empty directories - - -```python -subprocess.run( - f'find {extracted_path} -type f ! -name "*.tar.gz" -exec rm -f {{}} +', - shell=True, - check=True, -) -subprocess.run( - f"find {extracted_path} -type d -empty -exec rmdir {{}} +", shell=True, check=True -) -``` - - - - -
-``` -CompletedProcess(args='find /home/humbulani/.keras/datasets/census+income+kdd.zip -type d -empty -exec rmdir {} +', returncode=0) - -``` -
--- ## Define dataset metadata @@ -378,6 +354,10 @@ class GatedLinearUnit(layers.Layer): def call(self, inputs): return self.linear(inputs) * self.sigmoid(inputs) + # Remove build warnings + def build(self): + self.built = True + ``` --- @@ -415,6 +395,10 @@ class GatedResidualNetwork(layers.Layer): x = self.layer_norm(x) return x + # Remove build warnings + def build(self): + self.built = True + ``` --- @@ -490,52 +474,9 @@ class VariableSelection(layers.Layer): for idx, input in enumerate(concat_inputs): x.append(self.grns[idx](input)) x = keras.ops.stack(x, axis=1) - - # The reason for each individual backend calculation is that I couldn't find - # the equivalent keras operation that is backend-agnostic. In the following case there,s - # a keras.ops.matmul but it was returning errors. I could have used the tensorflow matmul - # for all backends, but due to jax jit tracing it results in an error. - def matmul_dependent_on_backend(thsi, v): - """ - Function for executing matmul for each backend. - """ - # jax backend - if keras.backend.backend() == "jax": - import jax.numpy as jnp - - result = jnp.sum(thsi * v, axis=1) - elif keras.backend.backend() == "torch": - result = torch.sum(thsi * v, dim=1) - # tensorflow backend - elif keras.backend.backend() == "tensorflow": - result = keras.ops.squeeze(tf.matmul(thsi, v, transpose_a=True), axis=1) - # unsupported backend exception - else: - raise ValueError( - "Unsupported backend: {}".format(keras.backend.backend()) - ) - return result - - # jax backend - if keras.backend.backend() == "jax": - # This repetative imports are intentional to force the idea of backend - # separation - import jax.numpy as jnp - - result_jax = matmul_dependent_on_backend(v, x) - return result_jax - # torch backend - if keras.backend.backend() == "torch": - import torch - - result_torch = matmul_dependent_on_backend(v, x) - return result_torch - # tensorflow backend - if keras.backend.backend() == "tensorflow": - import tensorflow as tf - - result_tf = keras.ops.squeeze(tf.matmul(v, x, transpose_a=True), axis=1) - return result_tf + return keras.ops.squeeze( + keras.ops.matmul(keras.ops.transpose(v, axes=[0, 2, 1]), x), axis=1 + ) # to remove the build warnings def build(self): @@ -568,7 +509,7 @@ def create_model(encoding_size): learning_rate = 0.001 dropout_rate = 0.15 batch_size = 265 -num_epochs = 1 # maybe adjusted to a desired value +num_epochs = 1 # may be adjusted to a desired value encoding_size = 16 model = create_model(encoding_size) @@ -579,94 +520,6 @@ model.compile( ) ``` -
-``` -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_7', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_8', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_9', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_10', 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( - -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_11', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_12', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_13', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_14', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_15', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_16', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_17', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_18', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_19', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_20', 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( - -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_21', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_22', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_23', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_24', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_25', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_26', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_27', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_28', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_29', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_30', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_31', 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( - -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_32', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_33', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_34', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_35', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_36', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_37', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_38', 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( -/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/layers/layer.py:391: UserWarning: `build()` was called on layer 'gated_residual_network_39', 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( - -``` -
Let's visualize our connectivity graph: @@ -708,1924 +561,1921 @@ Start training the model...
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accuracy: 0.9382 - loss: 302.3810  -634/Unknown 155s 244ms/step - accuracy: 0.9356 - loss: 310.7135 +633/Unknown 142s 224ms/step - accuracy: 0.9382 - loss: 302.3086  -635/Unknown 156s 244ms/step - accuracy: 0.9357 - loss: 310.6309 +634/Unknown 143s 224ms/step - accuracy: 0.9382 - loss: 302.2364  -636/Unknown 156s 244ms/step - accuracy: 0.9357 - loss: 310.5486 +635/Unknown 143s 224ms/step - accuracy: 0.9382 - loss: 302.1645  -637/Unknown 156s 244ms/step - accuracy: 0.9357 - loss: 310.4668 +636/Unknown 143s 224ms/step - accuracy: 0.9383 - loss: 302.0930  -638/Unknown 157s 245ms/step - accuracy: 0.9357 - loss: 310.3852 +637/Unknown 143s 224ms/step - accuracy: 0.9383 - loss: 302.0216  -639/Unknown 157s 245ms/step - accuracy: 0.9357 - loss: 310.3038 +638/Unknown 144s 224ms/step - accuracy: 0.9383 - loss: 301.9502  -640/Unknown 157s 245ms/step - accuracy: 0.9357 - loss: 310.2225 +639/Unknown 144s 224ms/step - accuracy: 0.9383 - loss: 301.8791 /home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:151: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset. self._interrupted_warning() @@ -2633,7 +2483,7 @@ Start training the model...  ```
- 640/640 ━━━━━━━━━━━━━━━━━━━━ 174s 271ms/step - accuracy: 0.9358 - loss: 310.1415 - val_accuracy: 0.9498 - val_loss: 227.1810 + 639/639 ━━━━━━━━━━━━━━━━━━━━ 160s 249ms/step - accuracy: 0.9383 - loss: 301.8082 - val_accuracy: 0.9485 - val_loss: 235.7996
@@ -2646,1145 +2496,1145 @@ Evaluating model performance...
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9/Unknown 2s 145ms/step - accuracy: 0.9441 - loss: 214.7674 + 9/Unknown 1s 133ms/step - accuracy: 0.9506 - loss: 218.5403  - 10/Unknown 2s 144ms/step - accuracy: 0.9446 - loss: 213.8222 + 10/Unknown 2s 133ms/step - accuracy: 0.9507 - loss: 217.4007  - 11/Unknown 2s 144ms/step - accuracy: 0.9452 - loss: 213.1922 + 11/Unknown 2s 134ms/step - accuracy: 0.9507 - loss: 216.4865  - 12/Unknown 2s 144ms/step - accuracy: 0.9455 - loss: 212.5974 + 12/Unknown 2s 133ms/step - accuracy: 0.9504 - loss: 215.7090  - 13/Unknown 2s 144ms/step - accuracy: 0.9457 - loss: 212.4554 + 13/Unknown 2s 135ms/step - accuracy: 0.9502 - loss: 215.4628  - 14/Unknown 2s 144ms/step - accuracy: 0.9459 - loss: 212.1609 + 14/Unknown 2s 135ms/step - accuracy: 0.9500 - loss: 215.0735  - 15/Unknown 2s 145ms/step - accuracy: 0.9461 - loss: 212.0100 + 15/Unknown 2s 134ms/step - accuracy: 0.9499 - loss: 214.8078  - 16/Unknown 3s 145ms/step - accuracy: 0.9464 - loss: 211.6899 + 16/Unknown 2s 134ms/step - accuracy: 0.9500 - loss: 214.3558  - 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accuracy: 0.9485 - loss: 229.1136  -361/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2299 +361/Unknown 50s 139ms/step - accuracy: 0.9485 - loss: 229.1216  -362/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2383 +362/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1297  -363/Unknown 54s 150ms/step - accuracy: 0.9490 - loss: 227.2464 +363/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1375  -364/Unknown 55s 150ms/step - accuracy: 0.9490 - loss: 227.2545 +364/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1453  -365/Unknown 55s 150ms/step - accuracy: 0.9490 - loss: 227.2623 +365/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1526  -366/Unknown 55s 150ms/step - accuracy: 0.9490 - loss: 227.2691 +366/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1591  -367/Unknown 55s 150ms/step - accuracy: 0.9490 - loss: 227.2757 +367/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1653  -368/Unknown 55s 149ms/step - accuracy: 0.9490 - loss: 227.2823 +368/Unknown 51s 139ms/step - accuracy: 0.9485 - loss: 229.1717  -369/Unknown 55s 149ms/step - accuracy: 0.9490 - loss: 227.2889 +369/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1778  -370/Unknown 55s 149ms/step - accuracy: 0.9490 - loss: 227.2951 +370/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1837  -371/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3009 +371/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1893  -372/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3066 +372/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1947  -373/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3122 +373/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.1999  -374/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3181 +374/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.2054  -375/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3240 +375/Unknown 52s 139ms/step - accuracy: 0.9485 - loss: 229.2110  -376/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3297 +376/Unknown 53s 139ms/step - accuracy: 0.9486 - loss: 229.2163  -377/Unknown 56s 149ms/step - accuracy: 0.9490 - loss: 227.3355 +377/Unknown 53s 139ms/step - accuracy: 0.9486 - loss: 229.2217  ```
- 377/377 ━━━━━━━━━━━━━━━━━━━━ 56s 149ms/step - accuracy: 0.9490 - loss: 227.3412 + 377/377 ━━━━━━━━━━━━━━━━━━━━ 53s 139ms/step - accuracy: 0.9486 - loss: 229.2270
``` -Test accuracy: 95.0% +Test accuracy: 94.94% ```