diff --git a/ch12/ch12.ipynb b/ch12/ch12.ipynb index b111af2e..57cbb6db 100644 --- a/ch12/ch12.ipynb +++ b/ch12/ch12.ipynb @@ -435,13 +435,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "README.md t10k-labels-idx1-ubyte\n", - "ch12.ipynb t10k-labels-idx1-ubyte.gz\n", - "ch12.py train-images-idx3-ubyte\n", - "\u001b[34mimages\u001b[m\u001b[m train-images-idx3-ubyte.gz\n", + "README.md t10k-images-idx3-ubyte.gz\n", + "ch12.ipynb t10k-labels-idx1-ubyte\n", + "ch12.py t10k-labels-idx1-ubyte.gz\n", + "\u001b[34mimages\u001b[m\u001b[m train-images-idx3-ubyte\n", + "mnist_scaled.npz train-images-idx3-ubyte.gz\n", "neuralnet.py train-labels-idx1-ubyte\n", - "t10k-images-idx3-ubyte train-labels-idx1-ubyte.gz\n", - "t10k-images-idx3-ubyte.gz\n" + "t10k-images-idx3-ubyte train-labels-idx1-ubyte.gz\n" ] } ], @@ -499,7 +499,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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aoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgaoEREJEgHlubBVapUKaxevXqKupK9li1b9mlhYWHV0j5P1zOarmdy6XomV1mvJ+iaFqWoa1qqAap69eosXbo0eb3KEQUFBRvK8jxdz2i6nsml65lcZb2eoGtalKKuqZb4REQkSBqgREQkSBqgREQkSKW6ByX55dNPPwXgF7/4hYvt3bsXgPfeey8jfRKR/KEZlIiIBEkzKIkzYsQI154yZQoAW7dudbEePXqkvU8ikp80gxIRkSBpgBIRkSBpiS+P7dy507W7dOkCwHPPPediBQUFADRu3NjF7rnnnjT1TkTynWZQIiISpKyYQX3//fcA7Nmzp9jHzZo1C4ifGbz11lsAjB8/3sWGDBkCwMSJE13skEMOAWDMmDEu1rdv3/3pdrAsfXzQoEEu9vzzzyc8bsaMGQD8/Oc/dzG7TiIh++abb1y7TZs2QPzWiP/85z8AVKpUKb0dk1LRDEpERIKkAUpERIKU0SW+HTt2APDdd9+5mE29/SWnzz//HIB777231O9hpe0HDhzoYtOnTwfgyCOPdLFzzz0XgGbNmpX6PbLNF198AcDcuXOLfZxdu9q1a6e6SyIl8uWXX0a2zaGHHgrAsmXLXGzhwoUAnHbaaS6mpersoBmUiIgEKe0zqI0bN7p2gwYNANi+fXtS3+OAA2Ljrs2W/G9Mffr0AaBatWoudthhhwFQtWqZziELniVGALRt2xaAwsLChMctXrzYtc8444zUdyzH/eUvf3Ht3bt3A7By5UoXu+uuuxKe07BhQ4C8Ojfok08+cW27JuvXr094nD8ziqoHaUlO/jW2z3nNmjVdzBKv8oVdy5kzZ7rYP//5TwBef/31hMc/8MADrn388ccDMG/ePBfr2bMnEFtlSRXNoEREJEgaoEREJEhpX+KrXLmyax9zzDFA2Zb4WrVqlfB6TzzxBAAVKlRwsaZNm5almznnwQcfdG1bGunevbuL2Z6www8/PL0dywFr164FYnvuIFaRY9q0aS4WtaRq1Tp8b775JgCnn366iy1fvjw5nQ3UokWLXPtPf/pTkY87+OCDXfu6664DYr/3EJ8MZewa9+/f38XyIUnCv6Zdu3YFYPPmzS5mn8dOnTq52IcffgjE/23Y9/EQKyCd6soymkGJiEiQ0j6D8r+52A27xx57zMXOPvtsADp37pzw3CZNmrj2X//6VwDKly/vYps2bQJgwoQJyetwlrOEiJdfftnFTj31VADGjh3rYpo5Jfrqq68AuPTSS13MtkH4bAXAT3u2b5v+DP6ll14q0fvaDXzbhpHLJk2aBMBNN92U8LMBAwa4tq229OvXz8UqVqwIxM+arOqJP1P40Y9+BMQfvJlr/KQPS4ho166di9ln+de//rWLjR49GohPHrEtP71793axhx56KOH9zjnnnCT0+odpBiUiIkHSACUiIkHKaCUJm47Xr1/fxWzJzp/y203TUaNGJTzOZ1P52267LfmdzSL+/hmryOHfjL/88ssBOOigg9LbsSzgJzrYcsj7779f6tex5WbbXwexZZbPPvvMxdq3bw9E7/k566yzSv2+2cauyddff+1ip5xyCgDDhg1zMf86mm3btgGxpSqIXXerKAEwefJkAA48MCtqY5fJiy++6NqtW7dO+PlFF10EwP333+9ifjKZeeWVV4DoZT1/z9MFF1xQ5r6WhmZQIiISpCC+UkSN5EcddVRCzN91b7XzotJ085VVKpg/f36xj6tSpQoARxxxRIle99FHH3XtqNnEzTffXNIuBm/kyJGuXdzMyU93nj17NgCNGjVysaiKJJYgdPfdd7tY1MzJkljuu+++EvY6e1n6s/8Zs5T6oUOHutjtt98OxB+5Y0kUc+bMcTG77n6iVMeOHZPd7WDY38QbbrjBxexvon/97Hc06m+t7/rrry/yZw8//LBrW4JKqmkGJSIiQdIAJSIiQQpiiS+KP9VcsmQJAE8++aSLrV69GoCf/exn6e1YwGxqb9cLYvsj/AK6tjwaxa84Ya/n36x+9913E54zePBgIHaMB2TfvqpVq1YBsQKaRalRowYAzz77bEKspD744INif96jRw8gfcsomXTccccB0Lx5cxezJT6/QsTFF18MwCWXXOJiUcVibV9V1D7KXDFlyhTXtqU9f+muW7duAPzud79zsaiEqL179wLxe/vWrVsHxFeNsGXETBSP1gxKRESCFOwMyk8jt4MK/Zv/duPT3xltO8X9FMh8SqKwFGmrsgGxmZP/LT8qOeKjjz4C4q+xX5rf2Mzo5JNPdjH7BtalSxcXsxuq/qGQIbvllluAWNqzz9+RbzfrSzprssQViM1sn3766WLfI5dv6u/LUr8rVaqU8DOrCwexlHv/m739bvtbUlq2bJmSfobAPkv+dhu7BjZrgvhU8n1Zaj7EUs/9FHVz5ZVXuvYVV1xRxh7vP82gREQkSBqgREQkSMEu8fmOPvpoIHaEAUCbNm0AGD9+vItZ25/i2s3SqJ3oucDfFxK1b8dOw7z22mtdzI4o8U/ZveOOOwCYMWOGi1mBTn/p7sYbbwTid/7XqVMHgC1btpTxX5F5lpTz8ccfu5jtqfGXOkv7OfJP1P3tb3+b8HOrpuKfYJqrn9XiWPWI0rAjIfxisSXd25eNrJCrXwjXjBs3zrV37twJxBfhtiX3V1991cUsqcm/DWJtqzYD0VV70kUzKBERCVJWzKDMmWee6dqWZu7voLbd6H6peEtFtW/+kH0p0MV55513XNtuevosBfyqq65yMfuGNWjQIBebO3cuEJ/UYN/4//CHP7iYzbr897LndOjQISGWLRo3bgyU/EiMH2Kp0ldffXXCz/yUX/v/k4+zJohtg5g3b56LRR3saPyjT2bNmpW6jgWoXLlyQKzmKMRqD9oqExSfGHbCCSe4tiWm+MkotmriH5aZSZpBiYhIkDRAiYhIkLJqic937LHHAvE3sG0Zq0WLFi5m+1vWrFnjYn7Rw2y3YsWKYn/uL+0ZS3qwozh8r732mmtb0VI/+cJiPrvGuVQ0dn9Z8kPUcsvjjz/u2ueff37a+hSivn37AjBt2jQXK26JKp/2Ne7LChTbkRgQ2x+2detWF6tbty4Qvxxq1Un8Y0js5/4Sn/3/CIVmUCIiEqSsnUEZ/9iDpk2bArGbiRCrN/XUU0+5mM2matWqlYYeppZ/+J3dXO7Vq1fC46xSBMQSTPyb0ZYO7c+QLCGibdu2Ce/hp09HJWfkIz/VN6oGorHZVb758ssvgfgVDDtSxJ8ZnXfeeUD8dfrzn/8MxG8DyFf+wYGWJFFSVmsPYn8T/c9o7dq1969zSaYZlIiIBEkDlIiIBClrl/hsqu+X5Ldd0ras5/OXC6Ju9OcCWyb5oRvJNqX3H7d06VIgvkT/rl27gPgjTexxP3QyZz6xHf52bSD6GtvOfjvRON8sW7YMiC9EavzTg+1IDb/qgS3xnXbaaansYs7zixdHfUb95fwQaAYlIiJByooZlKVQ3nPPPS5mNeM2btxY7HMtYcK/sZhLqar+cSN27IBfT89mRJYYAbBjx46E17Eb/H7ihO0qv/POO10sl6pw7I9vv/3Wta0KQtT2Bb+ShNWPzKXP3w/xt3dEHSJos6p69eq5mB150r9//4THl/ZwSInnX+dsoBmUiIgESQOUiIgEKbglPpveP/PMMy42cuRIANauXVui12jWrJlr2wmojRo1SlYXg+IXHrWCo/6psDVr1gRKvqwUVSy2QYMG+93PXGHHmwwYMMDFpk6dmvA4W+7zl7XyaWnP/OMf/3Dt7du3A/EnXjds2BCIJZoALFiwAIg//dWWnq2CjJTNypUrM92FUtEMSkREgpTRGZQd++DXgrJDyN54440SvUarVq1ce8SIEUB8Snmuf2u1AwkBFi5cCMRq40F8Gv6+/FmAzTDtGy3kbjr+/rAEk6hZk9VAA7jwwgvT1qeQ+VUKorZB2MxpyZIlLma1Iv10fKvz2LFjx9R1Ng9EHWoaMs2gREQkSBqgREQkSGlb4rOqBNdff72LWdl4/1TY4vhHEwwdOhSIv4HvJwzkI7sWdrKwJId/lMHYsWMTfl6/fn0AXnzxxbT1KVts3rw5IVatWjXXtqXQp59+OuFxfoJFKCe8Zjv/VPLiChqHItyeiYhIXkvJDGr9+vUA3HrrrS72wgsvALBhw4YSvUbFihVde9SoUQD069fPxcqXL7+/3RQpEfv8AUyaNCnh58OGDQPiU/Tlf2x26fMTTCx9vGrVqi5mqyPZVvUgG/hp+lZj8+2333Yxm/GedNJJ6e1YETSDEhGRIGmAEhGRIKVkie/xxx8HYPr06cU+zm58XnzxxbEOHfi/LlkVA4g/NVckXey00qjiukOGDHHtc845J219yjb+viUrYuwX0G3ZsiUQ2/sE0K1btzT1Lr+NHz8egNatW7uYFZyeOHGii1nR6EzQDEpERIKUkhnUwIED4/4rko3mzp0LwAMPPOBiVtvwmmuucTH/Br/E81c/evToEfdfyawmTZoA0LVrVxd75JFHgPgqHhMmTAAyk5imGZSIiARJA5SIiAQpuOM2RELRrl07AAYPHuxic+bMAbSsJ9mvQoUKQPwJ3LVq1QLi9/4NHz4cyEyyhGZQIiISJM2gRIpQp04dAPbu3Zvhnoikjs2kIFYVxf6baZpBiYhIkDRAiYhIkAqsWGOJHlxQsBUoWbXX/HJiYWFhqe+a63oWSdczuXQ9k6tM1xN0TYsReU1LNUCJiIiki5b4REQkSBqgREQkSBqgREQkSBqgREQkSBqgREQkSBqgREQkSBqgREQkSBqgREQkSBqgREQkSP8P783lTcv5Hc8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -511,7 +511,7 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,)\n", + "fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True)\n", "ax = ax.flatten()\n", "for i in range(10):\n", " img = X_train[y_train == i][0].reshape(28, 28)\n", @@ -540,7 +540,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -668,7 +668,7 @@ " minibatch_size : int (default: 1)\n", " Number of training examples per minibatch.\n", " seed : int (default: None)\n", - " Random seed for initalizing weights and shuffling.\n", + " Random seed for initializing weights and shuffling.\n", "\n", " Attributes\n", " -----------\n", @@ -949,7 +949,7 @@ { "data": { "text/plain": [ - "<__main__.NeuralNetMLP at 0x13a927b00>" + "<__main__.NeuralNetMLP at 0x7ffb285193c8>" ] }, "execution_count": 20, @@ -979,7 +979,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1008,7 +1008,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1026,8 +1026,8 @@ " label='Validation', linestyle='--')\n", "plt.ylabel('Accuracy')\n", "plt.xlabel('Epochs')\n", - "plt.legend()\n", - "#plt.savefig('images/12_08.png', dpi=300)\n", + "plt.legend(loc='lower right')\n", + "plt.savefig('images/12_08.png', dpi=300)\n", "plt.show()" ] }, @@ -1059,7 +1059,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1073,7 +1073,7 @@ "correct_lab = y_test[y_test != y_test_pred][:25]\n", "miscl_lab = y_test_pred[y_test != y_test_pred][:25]\n", "\n", - "fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,)\n", + "fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True)\n", "ax = ax.flatten()\n", "for i in range(25):\n", " img = miscl_img[i].reshape(28, 28)\n", @@ -1300,14 +1300,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[NbConvertApp] Converting notebook ch12.ipynb to script\n" + "[NbConvertApp] Converting notebook ch12.ipynb to script\n", + "[NbConvertApp] Writing 19664 bytes to ch12.py\n" ] } ], diff --git a/ch12/ch12.py b/ch12/ch12.py index 33f1d804..d581f644 100644 --- a/ch12/ch12.py +++ b/ch12/ch12.py @@ -198,7 +198,7 @@ def load_mnist(path, kind='train'): -fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,) +fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True) ax = ax.flatten() for i in range(10): img = X_train[y_train == i][0].reshape(28, 28) @@ -280,7 +280,7 @@ class NeuralNetMLP(object): minibatch_size : int (default: 1) Number of training examples per minibatch. seed : int (default: None) - Random seed for initalizing weights and shuffling. + Random seed for initializing weights and shuffling. Attributes ----------- @@ -577,8 +577,8 @@ def fit(self, X_train, y_train, X_valid, y_valid): label='Validation', linestyle='--') plt.ylabel('Accuracy') plt.xlabel('Epochs') -plt.legend() -#plt.savefig('images/12_08.png', dpi=300) +plt.legend(loc='lower right') +plt.savefig('images/12_08.png', dpi=300) plt.show() @@ -597,7 +597,7 @@ def fit(self, X_train, y_train, X_valid, y_valid): correct_lab = y_test[y_test != y_test_pred][:25] miscl_lab = y_test_pred[y_test != y_test_pred][:25] -fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True,) +fig, ax = plt.subplots(nrows=5, ncols=5, sharex=True, sharey=True) ax = ax.flatten() for i in range(25): img = miscl_img[i].reshape(28, 28) diff --git a/ch12/images/12_08.png b/ch12/images/12_08.png index f2de1737..0811de3e 100644 Binary files a/ch12/images/12_08.png and b/ch12/images/12_08.png differ diff --git a/ch12/neuralnet.py b/ch12/neuralnet.py index badfe2e2..5d73bde7 100644 --- a/ch12/neuralnet.py +++ b/ch12/neuralnet.py @@ -21,7 +21,7 @@ class NeuralNetMLP(object): minibatch_size : int (default: 1) Number of training examples per minibatch. seed : int (default: None) - Random seed for initalizing weights and shuffling. + Random seed for initializing weights and shuffling. Attributes ----------- @@ -56,7 +56,7 @@ def _onehot(self, y, n_classes): """ onehot = np.zeros((n_classes, y.shape[0])) - for idx, val in enumerate(y): + for idx, val in enumerate(y.astype(int)): onehot[val, idx] = 1. return onehot.T @@ -68,16 +68,16 @@ def _forward(self, X): """Compute forward propagation step""" # step 1: net input of hidden layer - # [n_samples, n_features] dot [n_features, n_hidden] - # -> [n_samples, n_hidden] + # [n_examples, n_features] dot [n_features, n_hidden] + # -> [n_examples, n_hidden] z_h = np.dot(X, self.w_h) + self.b_h # step 2: activation of hidden layer a_h = self._sigmoid(z_h) # step 3: net input of output layer - # [n_samples, n_hidden] dot [n_hidden, n_classlabels] - # -> [n_samples, n_classlabels] + # [n_examples, n_hidden] dot [n_hidden, n_classlabels] + # -> [n_examples, n_classlabels] z_out = np.dot(a_h, self.w_out) + self.b_out @@ -91,9 +91,9 @@ def _compute_cost(self, y_enc, output): Parameters ---------- - y_enc : array, shape = (n_samples, n_labels) + y_enc : array, shape = (n_examples, n_labels) one-hot encoded class labels. - output : array, shape = [n_samples, n_output_units] + output : array, shape = [n_examples, n_output_units] Activation of the output layer (forward propagation) Returns @@ -109,7 +109,7 @@ def _compute_cost(self, y_enc, output): term1 = -y_enc * (np.log(output)) term2 = (1. - y_enc) * np.log(1. - output) cost = np.sum(term1 - term2) + L2_term - + # If you are applying this cost function to other # datasets where activation # values maybe become more extreme (closer to zero or 1) @@ -123,7 +123,7 @@ def _compute_cost(self, y_enc, output): # # term1 = -y_enc * (np.log(output + 1e-5)) # term2 = (1. - y_enc) * np.log(1. - output + 1e-5) - + return cost def predict(self, X): @@ -131,12 +131,12 @@ def predict(self, X): Parameters ----------- - X : array, shape = [n_samples, n_features] + X : array, shape = [n_examples, n_features] Input layer with original features. Returns: ---------- - y_pred : array, shape = [n_samples] + y_pred : array, shape = [n_examples] Predicted class labels. """ @@ -149,13 +149,13 @@ def fit(self, X_train, y_train, X_valid, y_valid): Parameters ----------- - X_train : array, shape = [n_samples, n_features] + X_train : array, shape = [n_examples, n_features] Input layer with original features. - y_train : array, shape = [n_samples] + y_train : array, shape = [n_examples] Target class labels. - X_valid : array, shape = [n_samples, n_features] + X_valid : array, shape = [n_examples, n_features] Sample features for validation during training - y_valid : array, shape = [n_samples] + y_valid : array, shape = [n_examples] Sample labels for validation during training Returns: @@ -205,30 +205,30 @@ def fit(self, X_train, y_train, X_valid, y_valid): # Backpropagation ################## - # [n_samples, n_classlabels] + # [n_examples, n_classlabels] delta_out = a_out - y_train_enc[batch_idx] - # [n_samples, n_hidden] + # [n_examples, n_hidden] sigmoid_derivative_h = a_h * (1. - a_h) - # [n_samples, n_classlabels] dot [n_classlabels, n_hidden] - # -> [n_samples, n_hidden] + # [n_examples, n_classlabels] dot [n_classlabels, n_hidden] + # -> [n_examples, n_hidden] delta_h = (np.dot(delta_out, self.w_out.T) * sigmoid_derivative_h) - # [n_features, n_samples] dot [n_samples, n_hidden] + # [n_features, n_examples] dot [n_examples, n_hidden] # -> [n_features, n_hidden] grad_w_h = np.dot(X_train[batch_idx].T, delta_h) grad_b_h = np.sum(delta_h, axis=0) - # [n_hidden, n_samples] dot [n_samples, n_classlabels] + # [n_hidden, n_examples] dot [n_examples, n_classlabels] # -> [n_hidden, n_classlabels] grad_w_out = np.dot(a_h.T, delta_out) grad_b_out = np.sum(delta_out, axis=0) # Regularization and weight updates delta_w_h = (grad_w_h + self.l2*self.w_h) - delta_b_h = grad_b_h # bias is not regularized + delta_b_h = grad_b_h # bias is not regularized self.w_h -= self.eta * delta_w_h self.b_h -= self.eta * delta_b_h @@ -243,6 +243,7 @@ def fit(self, X_train, y_train, X_valid, y_valid): # Evaluation after each epoch during training z_h, a_h, z_out, a_out = self._forward(X_train) + cost = self._compute_cost(y_enc=y_train_enc, output=a_out) @@ -264,4 +265,4 @@ def fit(self, X_train, y_train, X_valid, y_valid): self.eval_['train_acc'].append(train_acc) self.eval_['valid_acc'].append(valid_acc) - return self + return self \ No newline at end of file