Remove tensorflow dependency. (#484)

* enh: remove tensorflow dependency. closes #481

* fix: ammend last commit

* fix: change params

* fix: checkout tutorial 07 on main and apply changes to reduce diff

docs/tutorials/07_gradient_descent.ipynb

@@ -755,8 +755,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow.data import Dataset"
+    "class Dataset:\n",
+    "    def __init__(self, inputs: np.ndarray, labels: np.ndarray):\n",
+    "        \"\"\"Simple Dataloader.\n",
+    "        \n",
+    "        Args:\n",
+    "            inputs: Array of shape (num_samples, num_dim)\n",
+    "            labels: Array of shape (num_samples,)\n",
+    "        \"\"\"\n",
+    "        assert len(inputs) == len(labels), \"Inputs and labels must have same length\"\n",
+    "        self.inputs = inputs\n",
+    "        self.labels = labels\n",
+    "        self.num_samples = len(inputs)\n",
+    "    \n",
+    "    def shuffle(self, seed=None):\n",
+    "        \"\"\"Shuffle the dataset in-place\"\"\"\n",
+    "        if seed is not None:\n",
+    "            np.random.seed(seed)\n",
+    "        indices = np.random.permutation(self.num_samples)\n",
+    "        self.inputs = self.inputs[indices]\n",
+    "        self.labels = self.labels[indices]\n",
+    "        return self\n",
+    "    \n",
+    "    def batch(self, batch_size):\n",
+    "        \"\"\"Create batches of the data\"\"\"\n",
+    "        for start in range(0, self.num_samples, batch_size):\n",
+    "            end = min(start + batch_size, self.num_samples)\n",
+    "            yield self.inputs[start:end], self.labels[start:end]"
    ]
   },
   {
@@ -768,9 +793,8 @@
    "source": [
     "batch_size = 4\n",
     "\n",
-    "tf.random.set_seed(1)\n",
-    "dataloader = Dataset.from_tensor_slices((inputs, labels))\n",
-    "dataloader = dataloader.shuffle(dataloader.cardinality()).batch(batch_size)"
+    "dataloader = Dataset(inputs, labels)\n",
+    "dataloader = dataloader.shuffle(seed=1).batch(batch_size)"
    ]
   },
   {