Update course book

neuromatch · Aug 21, 2024 · 47cc187 · 47cc187
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diff --git a/_sources/tutorials/W1D2_ComparingTasks/student/W1D2_Tutorial1.ipynb b/_sources/tutorials/W1D2_ComparingTasks/student/W1D2_Tutorial1.ipynb
@@ -737,7 +737,25 @@
     "\n",
     "        image_count += 1\n",
     "    fig.suptitle(f\"Training for {epochs} epochs with {N_train_data} points\")\n",
-    "    plt.show()"
+    "    plt.show()\n",
+    "\n",
+    "def cost_classification(output, target):\n",
+    "    criterion = nn.CrossEntropyLoss()\n",
+    "    target = target.to(torch.int64)\n",
+    "    cost = criterion(output, target)\n",
+    "    return cost\n",
+    "\n",
+    "def cost_regression(output, target):\n",
+    "    criterion = nn.MSELoss()\n",
+    "    cost = criterion(output, target)\n",
+    "    return cost\n",
+    "\n",
+    "def cost_autoencoder(output, target):\n",
+    "    criterion = nn.MSELoss()\n",
+    "    output_flat = output.view(output.size(0), -1)\n",
+    "    target_flat = target.view(target.size(0), -1)\n",
+    "    cost = criterion(output_flat, target_flat)\n",
+    "    return cost"
    ]
   },
   {
@@ -1351,14 +1369,11 @@
    },
    "outputs": [],
    "source": [
-    "def cost_classification(output, target):\n",
-    "\n",
-    "    ############################################################\n",
-    "    # Hint for batch_size: Get the first dimension of the target tensor\n",
-    "    # Hint for cost: Calculate the loss using the criterion\n",
-    "    raise NotImplementedError(\"Student has to fill in these lines\")\n",
-    "    ############################################################\n",
+    "############################################################\n",
+    "raise NotImplementedError(\"Student exercise: Calculate the loss using the criterion\")\n",
+    "############################################################\n",
     "\n",
+    "def cost_classification(output, target):\n",
     "    criterion = nn.CrossEntropyLoss()\n",
     "    target = target.to(torch.int64)\n",
     "    cost = ...\n",
@@ -1501,6 +1516,12 @@
     "triplet_flag_classification = False\n",
     "epochs_max_classification = 10\n",
     "\n",
+    "my_epoch_Classification = []\n",
+    "my_train_cost_Classification = [] # Add a list to store training costs\n",
+    "my_val_cost_Classification = [] # Add a list to store val costs\n",
+    "my_test_cost_Classification = [] # Add a list to store test costs\n",
+    "conf_matrices = [] # List to store confusion matrices\n",
+    "\n",
     "for N_train_data in training_points:\n",
     "    model = ClassificationConvNet(ConvNeuralNet(), ClassificationOutputLayer()).to(device)\n",
     "\n",
@@ -1522,16 +1543,10 @@
     "        N_train_data\n",
     "    )\n",
     "\n",
-    "    if N_train_data == 10:\n",
-    "        my_epoch_Classification = [my_epoch]\n",
-    "        my_train_cost_Classification = [my_train_cost]  # Add a list to store training costs\n",
-    "        my_val_cost_Classification = [my_val_cost] # Add a list to store val costs\n",
-    "        my_test_cost_Classification = [my_test_cost]  # Add a list to store test costs\n",
-    "    else:\n",
-    "        my_epoch_Classification.append(my_epoch)\n",
-    "        my_train_cost_Classification.append(my_train_cost)  # Append the training costs\n",
-    "        my_val_cost_Classification.append(my_val_cost)  # Append the training costs\n",
-    "        my_test_cost_Classification.append(my_test_cost)  # Append the training costs\n",
+    "    my_epoch_Classification.append(my_epoch)\n",
+    "    my_train_cost_Classification.append(my_train_cost)\n",
+    "    my_val_cost_Classification.append(my_val_cost)\n",
+    "    my_test_cost_Classification.append(my_test_cost)\n",
     "\n",
     "    # Compute predictions and confusion matrix for the validation set\n",
     "    all_preds = []\n",
@@ -1547,10 +1562,7 @@
     "\n",
     "    # Compute confusion matrix\n",
     "    conf_matrix = confusion_matrix(all_labels, all_preds)\n",
-    "    if N_train_data == 10:\n",
-    "        conf_matrices = [(N_train_data, conf_matrix)] # List to store confusion matrices\n",
-    "    else:\n",
-    "        conf_matrices.append((N_train_data, conf_matrix))  # Store the confusion matrix with the number of training points"
+    "    conf_matrices.append((N_train_data, conf_matrix))  # Store the confusion matrix with the number of training points"
    ]
   },
   {
@@ -1739,14 +1751,15 @@
    },
    "outputs": [],
    "source": [
+    "############################################################\n",
+    "# Hint for criterion: The criterion used for regression tasks is designed\n",
+    "# to minimize the average squared difference between predicted and actual values.\n",
+    "# Hint for cost: To compute the cost, apply the criterion function to\n",
+    "# the predicted output and the actual target values, which will return the mean squared error loss.\n",
+    "raise NotImplementedError(\"Student exercise\")\n",
+    "############################################################\n",
+    "\n",
     "def cost_regression(output, target):\n",
-    "    ############################################################\n",
-    "    # Hint for criterion: The criterion used for regression tasks is designed\n",
-    "    # to minimize the average squared difference between predicted and actual values.\n",
-    "    # Hint for cost: To compute the cost, apply the criterion function to\n",
-    "    # the predicted output and the actual target values, which will return the mean squared error loss.\n",
-    "    raise NotImplementedError(\"Student exercise\")\n",
-    "    ############################################################\n",
     "    criterion = ...\n",
     "    cost = ...\n",
     "    return cost"
@@ -1924,6 +1937,11 @@
     "triplet_flag = False\n",
     "epochs_max_regression = 10\n",
     "\n",
+    "my_epoch_Regression = []\n",
+    "my_train_cost_Regression = []\n",
+    "my_val_cost_Regression = []\n",
+    "my_test_cost_Regression = []\n",
+    "\n",
     "train_dataset_regression = RegressionMNIST(train_dataset)\n",
     "val_dataset_regression = RegressionMNIST(val_dataset)\n",
     "test_dataset_original_regression = RegressionMNIST(test_dataset_original)\n",
@@ -1938,16 +1956,10 @@
     "    optimizer = torch.optim.Adam(params=model.parameters(), lr=0.001)\n",
     "\n",
     "    my_epoch, my_train_cost, my_val_cost, my_test_cost = train(model, sampled_train_loader, sampled_val_loader, test_loader_original_regression, cost_regression, optimizer, epochs_max_regression, acc_flag, triplet_flag, task_name_regression, N_train_data)\n",
-    "    if N_train_data == 10:\n",
-    "        my_epoch_Regression = [my_epoch]\n",
-    "        my_train_cost_Regression= [my_train_cost]\n",
-    "        my_val_cost_Regression= [my_val_cost]\n",
-    "        my_test_cost_Regression= [my_test_cost]\n",
-    "    else:\n",
-    "        my_epoch_Regression.append(my_epoch)\n",
-    "        my_train_cost_Regression.append(my_train_cost)  # Append the training costs\n",
-    "        my_val_cost_Regression.append(my_val_cost)  # Append the val costs\n",
-    "        my_test_cost_Regression.append(my_test_cost)  # Append the test costs"
+    "    my_epoch_Regression.append(my_epoch)\n",
+    "    my_train_cost_Regression.append(my_train_cost)  # Append the training costs\n",
+    "    my_val_cost_Regression.append(my_val_cost)  # Append the val costs\n",
+    "    my_test_cost_Regression.append(my_test_cost)  # Append the test costs"
    ]
   },
   {
@@ -2115,14 +2127,15 @@
    },
    "outputs": [],
    "source": [
+    "############################################################\n",
+    "# Hint for output_flat: To flatten the output tensor for comparison, reshape it to\n",
+    "# have a size of (batch_size, -1) where batch_size is the number of samples.\n",
+    "# Hint for target_flat: Similarly, flatten the target tensor to match the shape\n",
+    "# of the flattened output tensor, ensuring it has a size of (batch_size, -1).\n",
+    "raise NotImplementedError(\"Student exercise\")\n",
+    "############################################################\n",
+    "\n",
     "def cost_autoencoder(output, target):\n",
-    "    ############################################################\n",
-    "    # Hint for output_flat: To flatten the output tensor for comparison, reshape it to\n",
-    "    # have a size of (batch_size, -1) where batch_size is the number of samples.\n",
-    "    # Hint for target_flat: Similarly, flatten the target tensor to match the shape\n",
-    "    # of the flattened output tensor, ensuring it has a size of (batch_size, -1).\n",
-    "    raise NotImplementedError(\"Student exercise\")\n",
-    "    ############################################################\n",
     "    criterion = nn.MSELoss()\n",
     "    output_flat = ...\n",
     "    target_flat = ...\n",
@@ -2267,6 +2280,12 @@
     "    shuffle=True\n",
     ")\n",
     "\n",
+    "my_epoch_Autoencoder = []\n",
+    "my_train_cost_Autoencoder = []\n",
+    "my_val_cost_Autoencoder = []\n",
+    "my_test_cost_Autoencoder = []\n",
+    "reconstructions = []\n",
+    "\n",
     "for N_train_data in training_points:\n",
     "    model = Autoencoder(ConvNeuralNet(), BottleneckLayer(M), ConvNeuralNetDecoder(M)).to(device)\n",
     "\n",
@@ -2291,16 +2310,10 @@
     "        task_name_autoencoder,\n",
     "        N_train_data\n",
     "    )\n",
-    "    if N_train_data == 10:\n",
-    "        my_epoch_Autoencoder = [my_epoch]\n",
-    "        my_train_cost_Autoencoder = [my_train_cost] # Add a list to store training costs\n",
-    "        my_val_cost_Autoencoder = [my_val_cost] # Add a list to store val costs\n",
-    "        my_test_cost_Autoencoder = [my_test_cost] # Add a list to store test costs\n",
-    "    else:\n",
-    "        my_epoch_Autoencoder.append(my_epoch)\n",
-    "        my_train_cost_Autoencoder.append(my_train_cost)\n",
-    "        my_val_cost_Autoencoder.append(my_val_cost)\n",
-    "        my_test_cost_Autoencoder.append(my_test_cost)\n",
+    "    my_epoch_Autoencoder.append(my_epoch)\n",
+    "    my_train_cost_Autoencoder.append(my_train_cost)\n",
+    "    my_val_cost_Autoencoder.append(my_val_cost)\n",
+    "    my_test_cost_Autoencoder.append(my_test_cost)\n",
     "\n",
     "    original_images = []\n",
     "    reconstructed_images = []\n",
@@ -2316,10 +2329,7 @@
     "                plot_reconstructions(original_images, reconstructed_images, N_train_data, epochs_max_autoencoder)\n",
     "                break\n",
     "\n",
-    "    if N_train_data == 10:\n",
-    "        reconstructions = [(N_train_data, original_images, reconstructed_images)] # List to store original and reconstructed images\n",
-    "    else:\n",
-    "        reconstructions.append((N_train_data, original_images, reconstructed_images))"
+    "    reconstructions.append((N_train_data, original_images, reconstructed_images))"
    ]
   },
   {
@@ -2605,6 +2615,12 @@
     "triplet_flag_inpainting = False\n",
     "epochs_max_inpainting = 10\n",
     "\n",
+    "my_epoch_Inpainting = []\n",
+    "my_train_cost_Inpainting = []\n",
+    "my_val_cost_Inpainting = []\n",
+    "my_test_cost_Inpainting = []\n",
+    "reconstructions_inpainting = []\n",
+    "\n",
     "# Create inpainting versions of the training, validation, and test datasets\n",
     "train_dataset_inpainting = InpaintingMNIST(train_dataset)\n",
     "val_dataset_inpainting = InpaintingMNIST(val_dataset)\n",
@@ -2642,17 +2658,11 @@
     "        task_name_inpainting,\n",
     "        N_train_data\n",
     "    )\n",
-    "    # Initialize lists to store training epochs and test costs for the inpainting task\n",
-    "    if N_train_data == 10:\n",
-    "        my_epoch_Inpainting = [my_epoch]\n",
-    "        my_train_cost_Inpainting = [my_train_cost]  # Add a list to store training costs\n",
-    "        my_val_cost_Inpainting = [my_val_cost] # Add a list to store val costs\n",
-    "        my_test_cost_Inpainting = [my_test_cost]  # Add a list to store test costs\n",
-    "    else:\n",
-    "        my_epoch_Inpainting.append(my_epoch)\n",
-    "        my_train_cost_Inpainting.append(my_train_cost)  # Append the training costs\n",
-    "        my_val_cost_Inpainting.append(my_val_cost)  # Append the training costs\n",
-    "        my_test_cost_Inpainting.append(my_test_cost)  # Append the training costs\n",
+    "\n",
+    "    my_epoch_Inpainting.append(my_epoch)\n",
+    "    my_train_cost_Inpainting.append(my_train_cost)\n",
+    "    my_val_cost_Inpainting.append(my_val_cost)\n",
+    "    my_test_cost_Inpainting.append(my_test_cost)\n",
     "    original_images = []\n",
     "    reconstructed_images = []\n",
     "    model.eval()\n",
@@ -2676,10 +2686,7 @@
     "              break\n",
     "              plt.suptitle(\"Training for 10 epochs with {} points\".format(N_train_data))\n",
     "\n",
-    "    if N_train_data == 10:\n",
-    "        reconstructions_inpainting = [(N_train_data, original_images, reconstructed_images)]\n",
-    "    else:\n",
-    "        reconstructions_inpainting.append((N_train_data, original_images, reconstructed_images))  # Store the original and reconstructed images"
+    "    reconstructions_inpainting.append((N_train_data, original_images, reconstructed_images))"
    ]
   },
   {
@@ -2886,7 +2893,7 @@
     "execution": {}
    },
    "source": [
-    "### Transfer example 1: regression to classification\n",
+    "## Transfer example 1: regression to classification\n",
     "\n"
    ]
   },
@@ -3499,7 +3506,7 @@
     "execution": {}
    },
    "source": [
-    "## Bonus discussion point 8\n",
+    "### Bonus discussion point 8\n",
     "\n",
     "\n",
     "How would you find out if the representations learned by the networks are similar or different, apart from their performance on downstream tasks?"
@@ -3520,7 +3527,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "###  Submit your feedback\n"
+    "####  Submit your feedback\n"
    ]
   },
   {

diff --git a/_sources/tutorials/W1D2_ComparingTasks/student/W1D2_Tutorial2.ipynb b/_sources/tutorials/W1D2_ComparingTasks/student/W1D2_Tutorial2.ipynb
@@ -724,6 +724,30 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7888898-2483-4705-97dc-a0099ef7b5cd",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "test_embeddings_untrained[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e6b3578-912c-4f96-9fd2-e6add908cb4a",
+   "metadata": {
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "test_embeddings_untrained[2]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/...rces/tutorials/W1D3_ComparingArtificialAndBiologicalNetworks/student/W1D3_Tutorial1.ipynb b/...rces/tutorials/W1D3_ComparingArtificialAndBiologicalNetworks/student/W1D3_Tutorial1.ipynb
@@ -128,8 +128,9 @@
    "source": [
     "# @title Install dependencies\n",
     "\n",
-    "!pip install -q ipympl ipywidgets mpl_interactions[\"jupyter\"] rsatoolbox torchlens\n",
+    "!pip install -q ipympl ipywidgets mpl_interactions[\"jupyter\"] torchlens\n",
     "!pip install -q graphviz\n",
+    "!pip install rsatoolbox==0.1.5\n",
     "\n",
     "# To install jupyter-matplotlib (ipympl) via pip\n",
     "!pip install -q torchlens\n",