solution for the affine and relu layers

bvahdat · Apr 5, 2021 · 242b757 · 242b757
1 parent 7a583f7
commit 242b757
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diff --git a/assignment2/FullyConnectedNets.ipynb b/assignment2/FullyConnectedNets.ipynb
@@ -62,7 +62,7 @@
    }
   },
   {
-   "execution_count": null,
+   "execution_count": 1,
    "cell_type": "code",
    "source": [
     "# As usual, a bit of setup\n",
@@ -97,7 +97,7 @@
    }
   },
   {
-   "execution_count": null,
+   "execution_count": 2,
    "cell_type": "code",
    "source": [
     "# Load the (preprocessed) CIFAR10 data.\n",
@@ -106,7 +106,15 @@
     "for k, v in list(data.items()):\n",
     "  print(('%s: ' % k, v.shape))"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "('X_train: ', (49000, 3, 32, 32))\n('y_train: ', (49000,))\n('X_val: ', (1000, 3, 32, 32))\n('y_val: ', (1000,))\n('X_test: ', (1000, 3, 32, 32))\n('y_test: ', (1000,))\n"
+     ]
+    }
+   ],
    "metadata": {
     "tags": [
      "pdf-ignore"
@@ -124,7 +132,7 @@
    "metadata": {}
   },
   {
-   "execution_count": null,
+   "execution_count": 3,
    "cell_type": "code",
    "source": [
     "# Test the affine_forward function\n",
@@ -148,7 +156,15 @@
     "print('Testing affine_forward function:')\n",
     "print('difference: ', rel_error(out, correct_out))"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Testing affine_forward function:\ndifference:  9.7698500479884e-10\n"
+     ]
+    }
+   ],
    "metadata": {}
   },
   {
@@ -160,7 +176,7 @@
    "metadata": {}
   },
   {
-   "execution_count": null,
+   "execution_count": 4,
    "cell_type": "code",
    "source": [
     "# Test the affine_backward function\n",
@@ -183,7 +199,15 @@
     "print('dw error: ', rel_error(dw_num, dw))\n",
     "print('db error: ', rel_error(db_num, db))"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Testing affine_backward function:\ndx error:  1.0908199508708189e-10\ndw error:  2.1752635504596857e-10\ndb error:  7.736978834487815e-12\n"
+     ]
+    }
+   ],
    "metadata": {}
   },
   {
@@ -195,7 +219,7 @@
    "metadata": {}
   },
   {
-   "execution_count": null,
+   "execution_count": 5,
    "cell_type": "code",
    "source": [
     "# Test the relu_forward function\n",
@@ -211,7 +235,15 @@
     "print('Testing relu_forward function:')\n",
     "print('difference: ', rel_error(out, correct_out))"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Testing relu_forward function:\ndifference:  4.999999798022158e-08\n"
+     ]
+    }
+   ],
    "metadata": {}
   },
   {
@@ -223,7 +255,7 @@
    "metadata": {}
   },
   {
-   "execution_count": null,
+   "execution_count": 6,
    "cell_type": "code",
    "source": [
     "np.random.seed(231)\n",
@@ -239,7 +271,15 @@
     "print('Testing relu_backward function:')\n",
     "print('dx error: ', rel_error(dx_num, dx))"
    ],
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Testing relu_backward function:\ndx error:  3.2756349136310288e-12\n"
+     ]
+    }
+   ],
    "metadata": {}
   },
   {
@@ -969,16 +1009,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3",
-   "language": "python"
+   "name": "python379jvsc74a57bd08bb14242992722f1f7be42bdaa541153855d10d2d8f9e79543206685130f6a1b",
+   "display_name": "Python 3.7.9 64-bit ('cs231n': conda)"
   },
   "language_info": {
    "mimetype": "text/x-python",
    "nbconvert_exporter": "python",
    "name": "python",
    "file_extension": ".py",
-   "version": "3.7.1",
+   "version": "3.7.9-final",
    "pygments_lexer": "ipython3",
    "codemirror_mode": {
     "version": 3,

diff --git a/assignment2/cs231n/layers.py b/assignment2/cs231n/layers.py
@@ -27,7 +27,9 @@ def affine_forward(x, w, b):
     ###########################################################################
     # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
 
-    pass
+    N = x.shape[0]
+    x_flattened = x.reshape(N, -1) # (N, D)
+    out = x_flattened.dot(w) + b  # (N, D) . (D, M) + (M,) = (N, M)
 
     # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
     ###########################################################################
@@ -60,7 +62,11 @@ def affine_backward(dout, cache):
     ###########################################################################
     # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
 
-    pass
+    dx = dout.dot(w.T).reshape(x.shape) # (N, M) . (M, D) = (N, D) => (N, d1, ..., d_k)
+    N = x.shape[0]
+    x_flattened = x.reshape(N, -1) # (N, D)
+    dw = x_flattened.T.dot(dout) # (D, N) . (N, M) = (D, M)
+    db = np.sum(dout.T, axis=1) # sum(M, N) => (M,)
 
     # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
     ###########################################################################
@@ -86,7 +92,7 @@ def relu_forward(x):
     ###########################################################################
     # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
 
-    pass
+    out = np.maximum(x, 0.) # (N, d1, ..., d_k)
 
     # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
     ###########################################################################
@@ -113,7 +119,9 @@ def relu_backward(dout, cache):
     ###########################################################################
     # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
 
-    pass
+    N = x.shape[0]
+    x_flattened = x.reshape(N, -1) # (N, D)
+    dx = dout * np.where(x_flattened > 0., 1., 0.) # (d1, ..., d_k) * (d1, ..., d_k) = (d1, ..., d_k)
 
     # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
     ###########################################################################