From 1db9841d336ab45f069fc777f1795dd07cf632f0 Mon Sep 17 00:00:00 2001
From: Supravisor <153783117+Supravisor@users.noreply.github.com>
Date: Mon, 12 Oct 2026 07:35:17 +1300
Subject: [PATCH 1/3] return input_function

---
 index.htm | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.htm b/index.htm
index 61e33ae..ea8f9e8 100644
--- a/index.htm
+++ b/index.htm
@@ -36,7 +36,7 @@ <h2>Linear regression</h2>
 
   <p>
     <input type="button" class="btn btn-success" value="list of features" onclick="document.editor.textbox.value+='\nCATEGORICAL_COLUMNS = [\'sex\', \'n_siblings_spouses\',\n    \'parch\', \'class\', \'deck\',\n    \'embark_town\', \'alone\']\n\nNUMERIC_COLUMNS = [\'age\', \'fare\']\n\nfeature_columns = []\nfor feature_name in CATEGORICAL_COLUMNS:\n    vocabulary = dftrain[feature_name].unique()  # gets a list of all unique values from given feature column\n    feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))\n\nfor feature_name in NUMERIC_COLUMNS:\n    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))\n'" />
-    <input type="button" class="btn btn-success" value="input function" onclick="document.editor.textbox.value+='\ndef make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n    def input_function():\n        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n        if shuffle:\n            ds = ds.shuffle(1000)\n        ds = ds.batch(batch_size).repeat(num_epochs)\n        return ds'" />
+    <input type="button" class="btn btn-success" value="input function" onclick="document.editor.textbox.value+='\ndef make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n    def input_function():\n        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n        if shuffle:\n            ds = ds.shuffle(1000)\n        ds = ds.batch(batch_size).repeat(num_epochs)\n        return ds\n    return input_function'" />
   </p>
 
 </div>

From 33a7913de8b091ca2f2cfd98a5b0b594d387d0d7 Mon Sep 17 00:00:00 2001
From: Supravisor <153783117+Supravisor@users.noreply.github.com>
Date: Mon, 12 Oct 2026 07:37:11 +1300
Subject: [PATCH 2/3] create train_input_fn variable

---
 index.htm | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.htm b/index.htm
index ea8f9e8..982871a 100644
--- a/index.htm
+++ b/index.htm
@@ -36,7 +36,7 @@ <h2>Linear regression</h2>
 
   <p>
     <input type="button" class="btn btn-success" value="list of features" onclick="document.editor.textbox.value+='\nCATEGORICAL_COLUMNS = [\'sex\', \'n_siblings_spouses\',\n    \'parch\', \'class\', \'deck\',\n    \'embark_town\', \'alone\']\n\nNUMERIC_COLUMNS = [\'age\', \'fare\']\n\nfeature_columns = []\nfor feature_name in CATEGORICAL_COLUMNS:\n    vocabulary = dftrain[feature_name].unique()  # gets a list of all unique values from given feature column\n    feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))\n\nfor feature_name in NUMERIC_COLUMNS:\n    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))\n'" />
-    <input type="button" class="btn btn-success" value="input function" onclick="document.editor.textbox.value+='\ndef make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n    def input_function():\n        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n        if shuffle:\n            ds = ds.shuffle(1000)\n        ds = ds.batch(batch_size).repeat(num_epochs)\n        return ds\n    return input_function'" />
+    <input type="button" class="btn btn-success" value="input function" onclick="document.editor.textbox.value+='\ndef make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n    def input_function():\n        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n        if shuffle:\n            ds = ds.shuffle(1000)\n        ds = ds.batch(batch_size).repeat(num_epochs)\n        return ds\n    return input_function\n\ntrain_input_fn = make_input_fn(dftrain, y_train)'" />
   </p>
 
 </div>

From 24c18be274833522365ce37338aa1efe80f7cae9 Mon Sep 17 00:00:00 2001
From: Supravisor <153783117+Supravisor@users.noreply.github.com>
Date: Mon, 12 Oct 2026 07:38:23 +1300
Subject: [PATCH 3/3] create eval_input_fn variable

---
 index.htm | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.htm b/index.htm
index 982871a..c64cee9 100644
--- a/index.htm
+++ b/index.htm
@@ -36,7 +36,7 @@ <h2>Linear regression</h2>
 
   <p>
     <input type="button" class="btn btn-success" value="list of features" onclick="document.editor.textbox.value+='\nCATEGORICAL_COLUMNS = [\'sex\', \'n_siblings_spouses\',\n    \'parch\', \'class\', \'deck\',\n    \'embark_town\', \'alone\']\n\nNUMERIC_COLUMNS = [\'age\', \'fare\']\n\nfeature_columns = []\nfor feature_name in CATEGORICAL_COLUMNS:\n    vocabulary = dftrain[feature_name].unique()  # gets a list of all unique values from given feature column\n    feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))\n\nfor feature_name in NUMERIC_COLUMNS:\n    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))\n'" />
-    <input type="button" class="btn btn-success" value="input function" onclick="document.editor.textbox.value+='\ndef make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n    def input_function():\n        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n        if shuffle:\n            ds = ds.shuffle(1000)\n        ds = ds.batch(batch_size).repeat(num_epochs)\n        return ds\n    return input_function\n\ntrain_input_fn = make_input_fn(dftrain, y_train)'" />
+    <input type="button" class="btn btn-success" value="input function" onclick="document.editor.textbox.value+='\ndef make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):\n    def input_function():\n        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))\n        if shuffle:\n            ds = ds.shuffle(1000)\n        ds = ds.batch(batch_size).repeat(num_epochs)\n        return ds\n    return input_function\n\ntrain_input_fn = make_input_fn(dftrain, y_train)\neval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)\n'" />
   </p>
 
 </div>