final fixes for lab2part1 (image logging on comet)

Author: Shorna Alam

lab2/Part1_MNIST.ipynb

@@ -61,27 +61,23 @@
       },
       "outputs": [],
       "source": [
-        "#Import Comet\n",
-        "%pip install comet_ml\n",
-        "import comet_ml\n",
-        "comet_ml.init(project_name=\"6.s191lab2.1.1\")\n",
-        "comet_model_1 = comet_ml.Experiment()\n",
-        "\n",
         "# Import Tensorflow 2.0\n",
         "%tensorflow_version 2.x\n",
         "import tensorflow as tf \n",
         "\n",
         "!pip install mitdeeplearning\n",
         "import mitdeeplearning as mdl\n",
         "\n",
+        "#Import Comet\n",
+        "!pip install comet_ml\n",
+        "import comet_ml\n",
+        "comet_ml.init(project_name=\"6.s191lab2_part1_NN\")\n",
+        "comet_model_1 = comet_ml.Experiment()\n",
+        "\n",
         "import matplotlib.pyplot as plt\n",
         "import numpy as np\n",
         "import random\n",
-        "from tqdm import tqdm\n",
-        "\n",
-        "# Check that we are using a GPU, if not switch runtimes\n",
-        "#   using Runtime > Change Runtime Type > GPU\n",
-        "assert len(tf.config.list_physical_devices('GPU')) > 0"
+        "from tqdm import tqdm"
       ]
     },
     {
@@ -421,7 +417,7 @@
       },
       "outputs": [],
       "source": [
-        "comet_ml.init(project_name=\"6.s191lab2.1.2\")\n",
+        "comet_ml.init(project_name=\"6.s191lab2_part1_CNN\")\n",
         "comet_model_2 = comet_ml.Experiment()\n",
         "\n",
         "'''TODO: Define the compile operation with your optimizer and learning rate of choice'''\n",
@@ -568,7 +564,8 @@
       "outputs": [],
       "source": [
         "print(\"Label of this digit is:\", test_labels[0])\n",
-        "plt.imshow(test_images[0,:,:,0], cmap=plt.cm.binary)"
+        "plt.imshow(test_images[0,:,:,0], cmap=plt.cm.binary)\n",
+        "comet_model_2.log_figure(figure=plt)"
       ]
     },
     {
@@ -594,7 +591,8 @@
         "plt.subplot(1,2,1)\n",
         "mdl.lab2.plot_image_prediction(image_index, predictions, test_labels, test_images)\n",
         "plt.subplot(1,2,2)\n",
-        "mdl.lab2.plot_value_prediction(image_index, predictions,  test_labels)"
+        "mdl.lab2.plot_value_prediction(image_index, predictions,  test_labels)\n",
+        "comet_model_2.log_figure(figure=plt)"
       ]
     },
     {
@@ -624,7 +622,9 @@
         "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
         "  mdl.lab2.plot_image_prediction(i, predictions, test_labels, test_images)\n",
         "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
-        "  mdl.lab2.plot_value_prediction(i, predictions, test_labels)\n"
+        "  mdl.lab2.plot_value_prediction(i, predictions, test_labels)\n",
+        "comet_model_2.log_figure(figure=plt)\n",
+        "comet_model_2.end()"
       ]
     },
     {
@@ -658,7 +658,7 @@
         "plotter = mdl.util.PeriodicPlotter(sec=2, xlabel='Iterations', ylabel='Loss', scale='semilogy')\n",
         "optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2) # define our optimizer\n",
         "\n",
-        "comet_ml.init(project_name=\"6.s191lab2.1.3\")\n",
+        "comet_ml.init(project_name=\"6.s191lab2_part1_CNN2\")\n",
         "comet_model_3 = comet_ml.Experiment()\n",
         "\n",
         "if hasattr(tqdm, '_instances'): tqdm._instances.clear() # clear if it exists\n",
@@ -686,6 +686,7 @@
         "  grads = # TODO\n",
         "  optimizer.apply_gradients(zip(grads, cnn_model.trainable_variables))\n",
         "\n",
+        "comet_model_3.log_figure(figure=plt)\n",
         "comet_model_3.end()\n"
       ]
     },

lab2/Part2_FaceDetection.ipynb

@@ -75,12 +75,6 @@
       },
       "outputs": [],
       "source": [
-        "# Import Comet\n",
-        "!pip install comet_ml\n",
-        "import comet_ml\n",
-        "comet_ml.init(project_name=\"6.s191lab2.2.1\")\n",
-        "comet_model_1 = comet_ml.Experiment()\n",
-        "\n",
         "# Import Tensorflow 2.0\n",
         "%tensorflow_version 2.x\n",
         "import tensorflow as tf\n",
@@ -91,10 +85,15 @@
         "import numpy as np\n",
         "from tqdm import tqdm\n",
         "\n",
-        "\n",
         "# Download and import the MIT Introduction to Deep Learning package\n",
         "!pip install mitdeeplearning\n",
-        "import mitdeeplearning as mdl"
+        "import mitdeeplearning as mdl\n",
+        "\n",
+        "# Import Comet\n",
+        "!pip install comet_ml\n",
+        "import comet_ml\n",
+        "comet_ml.init(project_name=\"6.s191lab2_part2_CNN\")\n",
+        "comet_model_1 = comet_ml.Experiment()"
       ]
     },
     {
@@ -185,7 +184,8 @@
         "\n",
         "plt.subplot(1, 2, 2)\n",
         "plt.imshow(not_face_images[idx_not_face])\n",
-        "plt.title(\"Not Face\"); plt.grid(False)"
+        "plt.title(\"Not Face\"); plt.grid(False)\n",
+        "comet_model_1.log_figure(figure=plt)"
       ]
     },
     {
@@ -310,6 +310,8 @@
         "    # Record the loss and plot the evolution of the loss as a function of training\n",
         "    loss_history.append(loss.numpy().mean())\n",
         "    plotter.plot(loss_history.get())\n",
+        "\n",
+        "comet_model_1.log_figure(figure=plt)\n",
         "comet_model_1.end()"
       ]
     },
@@ -764,7 +766,7 @@
       "source": [
         "### Training the SS-VAE ###\n",
         "\n",
-        "comet_ml.init(project_name=\"6.s191lab2.2.2\")\n",
+        "comet_ml.init(project_name=\"6.s191lab2_part2_VAE\")\n",
         "comet_model_2 = comet_ml.Experiment()\n",
         "\n",
         "# Hyperparameters\n",
@@ -821,9 +823,7 @@
         "    \n",
         "    # plot the progress every 200 steps\n",
         "    if j % 500 == 0: \n",
-        "      mdl.util.plot_sample(x, y, ss_vae)\n",
-        "  \n",
-        "comet_model_2.end()"
+        "      mdl.util.plot_sample(x, y, ss_vae)"
       ]
     },
     {
@@ -898,7 +898,9 @@
         "\n",
         "ax[1].imshow(mdl.util.create_grid_of_images(x[ind[-25:]]))\n",
         "ax[1].set_title(\"Samples with the highest reconstruction loss \\n\" + \n",
-        "                f\"Average recon loss: {np.mean(vae_loss[ind[-25:]]):.2f}\");"
+        "                f\"Average recon loss: {np.mean(vae_loss[ind[-25:]]):.2f}\");\n",
+        "\n",
+        "comet_model_2.log_figure()"
       ]
     },
     {
@@ -964,7 +966,9 @@
         "# Visualize all of the reconstructions!\n",
         "ax[1].imshow(mdl.util.create_grid_of_images(recons, (1, num_steps)))\n",
         "ax[1].set_xlabel(\"Latent step\")\n",
-        "ax[1].set_ylabel(\"Visualization\");\n"
+        "ax[1].set_ylabel(\"Visualization\");\n",
+        "\n",
+        "comet_model_2.log_figure()"
       ]
     },
     {
@@ -1013,7 +1017,10 @@
         "# Plot the results\n",
         "plt.plot(np.linspace(np.min(z_mean), np.max(z_mean), num_steps+1), accuracy_per_latent,'-o')\n",
         "plt.xlabel(\"Latent step\")\n",
-        "plt.ylabel(\"Relative accuracy\")"
+        "plt.ylabel(\"Relative accuracy\")\n",
+        "\n",
+        "comet_model_2.log_figure()\n",
+        "comet_model_2.end()"
       ]
     },
     {