Assignment 1

01_materials/labs/lab_1.ipynb

@@ -249,6 +249,15 @@
     "Because of this, we need to convert our labels from an integer value into a one-hot encoded vector. This means that each label will be represented as a vector of length 10, with a 1 in the position corresponding to the class, and 0s everywhere else. For example, the label 9 would be represented as `[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]`. This is a common way of representing categorical data in machine learning. By doing this, we ensure that our model is taught the correct relationship between the classes."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install tensorflow"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -482,7 +491,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here - copy the relevant parts from the previous section and add more cells as needed"
+    "from tensorflow.keras import optimizers\n",
+    "model = Sequential()\n",
+    "\n",
+    "# Input layer\n",
+    "model.add(Dense(64, activation='relu', input_shape=(64,))) # 64 neurons, ReLU activation, input shape of 64\n",
+    "\n",
+    "# Hidden layer\n",
+    "model.add(Dense(64, activation='relu')) # 64 neurons, ReLU activation\n",
+    "\n",
+    "# Output layer\n",
+    "model.add(Dense(10, activation='softmax')) # 10 neurons, softmax activation\n",
+    "\n",
+    "model.summary()\n",
+    "\n",
+    "model.compile(optimizer=optimizers.SGD(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "history = model.fit(X_train, y_train, epochs=15, batch_size=32)"
    ]
   },
   {
@@ -504,7 +528,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "# Your code hefrom tensorflow.keras import optimizers\n",
+    "model = Sequential()\n",
+    "\n",
+    "# Input layer\n",
+    "model.add(Dense(64, activation='relu', input_shape=(64,))) # 64 neurons, ReLU activation, input shape of 64\n",
+    "\n",
+    "# Hidden layer\n",
+    "model.add(Dense(64, activation='relu')) # 64 neurons, ReLU activation\n",
+    "\n",
+    "# Output layer\n",
+    "model.add(Dense(10, activation='softmax')) # 10 neurons, softmax activation\n",
+    "\n",
+    "model.summary()\n",
+    "\n",
+    "model.compile(optimizer=optimizers.SGD(learning_rate=0.01, momentum=0.9),loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "history = model.fit(X_train, y_train, epochs=15, batch_size=32)"
    ]
   },
   {
@@ -798,18 +837,6 @@
    "language": "python",
    "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  },
   "mimetype": "text/x-python",
   "name": "python",
   "npconvert_exporter": "python",