2018-01-01 20:33:05 +01:00
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{
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2018-01-01 20:46:31 +01:00
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"cells": [
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{
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"cell_type": "code",
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2018-01-15 23:01:56 +01:00
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"execution_count": 1,
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2018-01-01 20:46:31 +01:00
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"metadata": {},
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2018-01-15 23:01:56 +01:00
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2018-01-15 23:55:38 +01:00
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"{'i': {'count': 75, 'index': 4, 'vector': [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]}, 'a': {'count': 36, 'index': 6, 'vector': [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]}, 'd': {'count': 45, 'index': 5, 'vector': [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]}, 'l': {'count': 64, 'index': 0, 'vector': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, 't': {'count': 64, 'index': 2, 'vector': [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, 'n': {'count': 75, 'index': 7, 'vector': [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]}, 'c': {'count': 30, 'index': 8, 'vector': [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]}, 'e': {'count': 117, 'index': 1, 'vector': [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, 's': {'count': 40, 'index': 9, 'vector': [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]}, 'u': {'count': 55, 'index': 3, 'vector': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]}, 'r': {'count': 42, 'index': 10, 'vector': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]}, 'o': {'count': 39, 'index': 11, 'vector': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]}}\n",
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2018-01-15 23:01:56 +01:00
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"12\n",
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"1\n",
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"number of batches=1\n",
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"size of first batch=577,577\n",
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"size of last batch=577,577\n",
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"300 9\n"
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]
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}
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],
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"source": [
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2018-01-15 23:55:38 +01:00
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"from letters import LettersData\n",
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"data = LettersData(\"data/\", \"list_expected_data.txt\")\n",
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2018-01-15 23:01:56 +01:00
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"data.process()\n",
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"#x,y = data.get_data()\n",
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"#classes = data.get_classes()\n",
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"#ysize = data.get_class_element_size()\n",
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"#xsize = data.get_input_image_size()\n",
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"#print(len(classes))\n",
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"#print(len(classes['P']))\n",
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"#print(xsize,ysize)\n",
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"#print(len(x))\n",
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"#print(len(y))\n",
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"#print(y[5])\n",
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"#print(data.get_vocab())\n",
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"v=data.get_vocab_with_min_count(30)\n",
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"print(v)\n",
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"print(len(v))\n",
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"batch = data.get_batches()\n",
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"print(len(batch))\n",
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"batch = data.get_batches(min_count=40, mini_batch_size=577)\n",
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"print(\"number of batches={}\".format(len(batch)))\n",
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"print(\"size of first batch={},{}\".format(len(batch[0][0]),len(batch[0][1])))\n",
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"print(\"size of last batch={},{}\".format(len(batch[-1][0]),len(batch[-1][1])))\n",
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"xsize = len(batch[0][0][0])\n",
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"ysize = len(batch[0][1][0])\n",
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"print(xsize, ysize)"
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]
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},
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{
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"cell_type": "code",
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2018-01-15 23:55:38 +01:00
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"execution_count": 2,
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2018-01-15 23:01:56 +01:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0 2 4 6 9]\n",
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" [1 3 5 7 8]]\n",
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"[[0 0 0 0 1]\n",
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" [1 1 1 1 0]]\n",
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"[[11 2 13 4]\n",
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" [ 5 6 7 8]]\n",
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"[[1 0 1 0]\n",
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" [0 1 0 1]]\n",
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"1\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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2018-01-01 20:46:31 +01:00
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"\n",
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2018-01-15 23:01:56 +01:00
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"def output_to_hard(output):\n",
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" m = output.shape[1]\n",
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" hard = np.zeros_like(output)\n",
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" hard[output.argmax(0), np.arange(m)] = 1\n",
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" return hard\n",
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2018-01-01 20:46:31 +01:00
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"\n",
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"\n",
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2018-01-15 23:01:56 +01:00
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"def count_errors(output, expected_output):\n",
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" \"\"\"Count the number of patterns different assuming dimension (n, m)\n",
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" having m patterns of size n\n",
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" \"\"\"\n",
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" # check differences\n",
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" err = np.equal(output,expected_output)\n",
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" # invert such as true means not equal\n",
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" ierr = np.invert(err)\n",
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" # count number of bad values for each column\n",
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" nb = np.count_nonzero(ierr, axis=0)\n",
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" # count number of errors\n",
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" nb_err = np.count_nonzero(nb)\n",
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" return nb_err\n",
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" \n",
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"\n",
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"\n",
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"a = np.array([[0, 1], [2, 3], [4, 5], [6, 7], [9, 8]]).T\n",
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"print(a)\n",
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"print(output_to_hard(a))\n",
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"a = np.array([[11,2,13,4],[5,6,7,8]])\n",
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"print(a)\n",
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"b = np.array([[1, 0, 1, 0], [1, 1, 0, 1]])\n",
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"h = output_to_hard(a)\n",
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"print(h)\n",
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"\n",
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"print(count_errors(h, b))\n"
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2018-01-01 20:46:31 +01:00
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]
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},
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{
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"cell_type": "code",
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2018-01-15 23:55:38 +01:00
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"execution_count": 3,
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2018-01-01 20:46:31 +01:00
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"metadata": {},
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"outputs": [
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2018-01-15 23:01:56 +01:00
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0. 3.]\n",
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"False\n",
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"True\n"
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]
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}
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],
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"source": [
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"def all_axis_equal(n, axis=0):\n",
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" \"\"\"\n",
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" :param axis: if 0 check that all columns are equal, if 1 chech rows\n",
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" \"\"\"\n",
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" if axis == 0:\n",
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" res = np.std(n, axis=1)\n",
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" else:\n",
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" res = np.std(n)\n",
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" res = np.sum(res)\n",
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" return res == 0.\n",
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"\n",
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"\n",
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"def axis_equal(n, axis=0):\n",
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" \"\"\"\n",
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" :param axis: if 0 check that all columns are equal, if 1 chech rows\n",
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" \"\"\"\n",
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" if axis == 0:\n",
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" res = np.std(n, axis=1)\n",
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" else:\n",
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" res = np.std(n)\n",
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" return res\n",
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"\n",
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"\n",
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"a=np.array([[1,-2,3,4],[5,6,-7,8]])\n",
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"all_axis_equal(a)\n",
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"a=np.array([[1,-2,3,4],[1,6,-7,8]])\n",
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"all_axis_equal(a) \n",
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"\n",
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"a = np.array([[-1., -1.], [-3., 3]])\n",
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"print(np.std(a,axis=1))\n",
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"\n",
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"b=np.array([[-2,-2,-2],[5,6,-7]])\n",
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"print(all_axis_equal(b))\n",
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"b=np.array([[-2,-2,-2],[5,5,5]])\n",
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"print(all_axis_equal(b))"
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]
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},
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{
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"cell_type": "code",
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2018-01-15 23:55:38 +01:00
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"execution_count": 5,
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2018-01-15 23:01:56 +01:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2018-01-15 23:55:38 +01:00
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"[0, 0, 0, 0, 0, 0, 0, 0, 1]\n",
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2018-01-15 23:01:56 +01:00
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"tanh factor=0.05773502691896258\n",
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"tanh factor=0.023570226039551584\n",
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"tanh factor=0.040824829046386304\n",
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"tanh factor=0.05773502691896258\n",
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"softmax factor=0.01\n",
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2018-01-15 23:55:38 +01:00
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"nb errors before training=561/577\n",
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2018-01-15 23:01:56 +01:00
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"Training...\n"
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]
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},
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2018-01-03 14:43:31 +01:00
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{
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"data": {
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2018-01-15 23:55:38 +01:00
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"image/png": "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2018-01-03 14:43:31 +01:00
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"text/plain": [
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2018-01-15 23:55:38 +01:00
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"<matplotlib.figure.Figure at 0x7f00f24c2710>"
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2018-01-03 14:43:31 +01:00
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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2018-01-01 20:46:31 +01:00
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2018-01-15 23:55:38 +01:00
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"learning duration=311.9925892353058(s)\n",
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"{'iterations': 400, 'cost_function': 0.01926710779513728}\n",
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"nb errors=0/577\n"
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2018-01-15 23:01:56 +01:00
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]
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}
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],
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"source": [
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|
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"from mlp import MultiLayerPerceptron\n",
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"import time\n",
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"import numpy as np\n",
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"X, Y = np.array(batch[0][0]), np.array(batch[0][1])\n",
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"xsize = len(batch[0][0][0])\n",
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"ysize = len(batch[0][1][0])\n",
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"print(batch[0][1][0])\n",
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"m = len(batch[0][0])\n",
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"mlp = MultiLayerPerceptron(L=5, n=[xsize, 1800, 600, 300, 30, ysize], g=[\"tanh\"]*4 + [\"softmax\"], alpha=0.001, set_random_w=False)# w_rand_factor=0.01)\n",
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"mlp.init_random_weights(use_formula=True)\n",
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"#mlp.use_regularization(0.1)\n",
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"mlp.use_adam()\n",
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"\n",
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"# Compute output\n",
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"output = mlp.compute_outputs(X.T)\n",
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"hard_output = output_to_hard(output)\n",
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"mlp.set_all_expected_output_examples(Y.T)\n",
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"expected_output = mlp.get_expected_output()\n",
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|
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"print(\"nb errors before training={}/{}\".format(count_errors(hard_output, expected_output), output.shape[1]))\n",
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"\n",
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|
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"# Proceed learning with gradient descent\n",
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"print(\"Training...\")\n",
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|
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"t0 = time.time()\n",
|
2018-01-15 23:55:38 +01:00
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|
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"res = mlp.learning(X.T, Y.T, m, min_cost=0.005, max_iter=400, plot=True)\n",
|
2018-01-15 23:01:56 +01:00
|
|
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"t1 = time.time()\n",
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|
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"print(\"learning duration={}(s)\".format(t1-t0))\n",
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"print(res)\n",
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"\n",
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|
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"output = mlp.get_output()\n",
|
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|
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"hard_output = output_to_hard(output)\n",
|
|
|
|
|
"expected_output = mlp.get_expected_output()\n",
|
|
|
|
|
"print(\"nb errors={}/{}\".format(count_errors(hard_output, expected_output), output.shape[1]))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
|
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"name": "stdout",
|
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"output_type": "stream",
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"text": [
|
|
|
|
|
"[[0 1 0 ... 0 0 0]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]\n",
|
|
|
|
|
" [1 0 0 ... 1 0 0]\n",
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|
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|
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" ...\n",
|
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" [0 0 0 ... 0 1 0]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 1]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]]\n",
|
|
|
|
|
"out=[[1.20974679e-03 9.94810203e-01 1.81144828e-03 ... 1.21511565e-03\n",
|
|
|
|
|
" 9.06859226e-04 1.31348417e-04]\n",
|
|
|
|
|
" [1.89512803e-03 2.11689863e-04 7.43912078e-05 ... 1.89605227e-03\n",
|
|
|
|
|
" 5.79609227e-03 3.18173844e-04]\n",
|
|
|
|
|
" [9.81964661e-01 6.98882514e-04 3.10199130e-06 ... 9.81971394e-01\n",
|
|
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|
|
" 8.67877832e-04 5.21368855e-06]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [1.15257668e-03 7.73124214e-04 4.52359346e-05 ... 1.15608559e-03\n",
|
|
|
|
|
" 9.75057094e-01 1.49883192e-05]\n",
|
|
|
|
|
" [8.41201862e-06 8.08946746e-05 1.13639649e-02 ... 8.41791178e-06\n",
|
|
|
|
|
" 9.76566627e-06 9.80475500e-01]\n",
|
|
|
|
|
" [8.87554173e-05 3.38173023e-05 2.15643124e-03 ... 8.91617413e-05\n",
|
|
|
|
|
" 2.32828424e-05 3.03532518e-03]]\n",
|
|
|
|
|
"alleq? = False\n",
|
|
|
|
|
"eq? = [0.39963611 0.31029355 0.25476702 0.3301145 0.25201999 0.30368108\n",
|
|
|
|
|
" 0.32692957 0.28733344 0.26546751]\n",
|
|
|
|
|
"hout=[[0. 1. 0. ... 0. 0. 0.]\n",
|
|
|
|
|
" [0. 0. 0. ... 0. 0. 0.]\n",
|
|
|
|
|
" [1. 0. 0. ... 1. 0. 0.]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [0. 0. 0. ... 0. 1. 0.]\n",
|
|
|
|
|
" [0. 0. 0. ... 0. 0. 1.]\n",
|
|
|
|
|
" [0. 0. 0. ... 0. 0. 0.]]\n",
|
|
|
|
|
"alleq? = False\n",
|
|
|
|
|
"expout=[[0 1 0 ... 0 0 0]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]\n",
|
|
|
|
|
" [1 0 0 ... 1 0 0]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [0 0 0 ... 0 1 0]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 1]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]]\n",
|
|
|
|
|
"nb errors=0\n",
|
|
|
|
|
"ieq? = [0. 0. 0. 0. 0.04159445 0.04159445\n",
|
|
|
|
|
" 0. 0.04159445 0.05877236 0. 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0.05877236 0.14840052\n",
|
|
|
|
|
" 0.05877236 0.12391378 0.15912484 0.10947391 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0.1996606 0.30524256\n",
|
|
|
|
|
" 0.17384666 0.15912484 0.21848089 0.18292235 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0.25400445 0.34916742\n",
|
|
|
|
|
" 0.24497509 0.15386591 0.25400445 0.23548517 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0.22201304 0.36283563\n",
|
|
|
|
|
" 0.29365729 0.11692983 0.26540807 0.26262075 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0.17845063 0.32849489\n",
|
|
|
|
|
" 0.30298078 0.10144201 0.26540807 0.26540807 0. 0.\n",
|
|
|
|
|
" 0. 0.05877236 0.04159445 0.04159445 0.17845063 0.34738218\n",
|
|
|
|
|
" 0.31403124 0.15912484 0.27086342 0.26540807 0. 0.\n",
|
|
|
|
|
" 0. 0.14270495 0.17384666 0.21848089 0.36283563 0.46536942\n",
|
|
|
|
|
" 0.44766404 0.35613459 0.32243155 0.27086342 0. 0.\n",
|
|
|
|
|
" 0. 0.25692035 0.32447451 0.42867576 0.4758936 0.49479993\n",
|
|
|
|
|
" 0.48113491 0.4758936 0.43766237 0.30968748 0. 0.\n",
|
|
|
|
|
" 0. 0.34191784 0.42971151 0.49772331 0.49966874 0.49945229\n",
|
|
|
|
|
" 0.48113491 0.49401623 0.48304144 0.39947913 0. 0.\n",
|
|
|
|
|
" 0.04159445 0.38438942 0.48395465 0.4981938 0.49918159 0.49978294\n",
|
|
|
|
|
" 0.46932204 0.4844013 0.49772331 0.44142084 0. 0.\n",
|
|
|
|
|
" 0.10144201 0.37549571 0.49681727 0.49719801 0.49804305 0.49772331\n",
|
|
|
|
|
" 0.43474966 0.39005198 0.49479993 0.45429764 0. 0.\n",
|
|
|
|
|
" 0.13050156 0.37241944 0.49945229 0.49936808 0.47963429 0.49620022\n",
|
|
|
|
|
" 0.43474966 0.36609093 0.47963429 0.45104631 0.04159445 0.\n",
|
|
|
|
|
" 0.13050156 0.36928501 0.49993917 0.49598225 0.44592287 0.49317647\n",
|
|
|
|
|
" 0.43474966 0.34191784 0.46995521 0.45104631 0.04159445 0.\n",
|
|
|
|
|
" 0.05877236 0.33436749 0.49873612 0.49873612 0.45818189 0.48888231\n",
|
|
|
|
|
" 0.43276226 0.36769552 0.47533153 0.45021313 0.04159445 0.\n",
|
|
|
|
|
" 0. 0.29125401 0.49031788 0.49966874 0.49978294 0.49945229\n",
|
|
|
|
|
" 0.47301311 0.45968067 0.48612205 0.44937172 0.13675044 0.\n",
|
|
|
|
|
" 0. 0.22547654 0.42657558 0.49132761 0.49788622 0.49993917\n",
|
|
|
|
|
" 0.48527482 0.46671642 0.45104631 0.4189163 0.17384666 0.\n",
|
|
|
|
|
" 0. 0.11692983 0.31403124 0.43376056 0.45893516 0.46671642\n",
|
|
|
|
|
" 0.43474966 0.37549571 0.34005922 0.33243052 0.07191852 0.\n",
|
|
|
|
|
" 0. 0.04159445 0.15386591 0.23870299 0.31403124 0.35441835\n",
|
|
|
|
|
" 0.33243052 0.22201304 0.21487672 0.20359342 0.04159445 0.\n",
|
|
|
|
|
" 0. 0.04159445 0.04159445 0.04159445 0.05877236 0.08297199\n",
|
|
|
|
|
" 0.07191852 0. 0. 0. 0. 0.\n",
|
|
|
|
|
" 0. 0.04159445 0.04159445 0.04159445 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0. 0.\n",
|
|
|
|
|
" 0. 0.04159445 0.04159445 0.04159445 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0. 0. 0.\n",
|
|
|
|
|
" 0. 0. 0. 0.04159445 0.04159445 0.04159445\n",
|
|
|
|
|
" 0. 0. 0. 0. 0. 0. ]\n",
|
|
|
|
|
"i? = [[0 0 0 ... 0 0 0]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]\n",
|
|
|
|
|
" [0 0 0 ... 0 0 0]]\n",
|
|
|
|
|
"diff exp= [117 64 42 75 40 64 75 55 45]\n",
|
|
|
|
|
"[[-0.01319087 0.03983991 -0.05525597 ... -0.03207711 0.06692414\n",
|
|
|
|
|
" -0.06375975]\n",
|
|
|
|
|
" [ 0.03026753 0.01874685 0.00903375 ... 0.01605231 -0.08379402\n",
|
|
|
|
|
" 0.04562931]\n",
|
|
|
|
|
" [ 0.04805276 0.05503196 -0.00389218 ... 0.0183783 0.01674333\n",
|
|
|
|
|
" 0.02185625]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [ 0.02624143 0.03224954 -0.00911125 ... -0.08354134 0.03582595\n",
|
|
|
|
|
" 0.05569943]\n",
|
|
|
|
|
" [-0.02090887 -0.09105613 -0.09100615 ... -0.01404412 0.03874845\n",
|
|
|
|
|
" -0.06491289]\n",
|
|
|
|
|
" [-0.01343145 -0.03273509 0.08220062 ... -0.03275636 -0.00326426\n",
|
|
|
|
|
" -0.03555161]]\n",
|
|
|
|
|
"float64\n",
|
|
|
|
|
"[[-0.01420414 -0.07207896 0.05688704 ... 0.01537264 0.0084524\n",
|
|
|
|
|
" -0.03883495]\n",
|
|
|
|
|
" [-0.02508493 0.05414954 -0.03563722 ... -0.0120563 0.02642832\n",
|
|
|
|
|
" 0.00609925]\n",
|
|
|
|
|
" [ 0.02473474 -0.05099813 0.07484969 ... 0.04273742 0.02562809\n",
|
|
|
|
|
" -0.07692152]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [-0.03358954 -0.01829128 -0.00578833 ... 0.01829952 0.04571721\n",
|
|
|
|
|
" 0.0090804 ]\n",
|
|
|
|
|
" [-0.02371952 -0.0468924 -0.00054122 ... -0.07090847 -0.10107239\n",
|
|
|
|
|
" 0.05067513]\n",
|
|
|
|
|
" [ 0.06595318 -0.01398758 0.05028443 ... 0.05333875 0.04927292\n",
|
|
|
|
|
" -0.02119925]]\n",
|
|
|
|
|
"float64\n",
|
|
|
|
|
"[[-8.55127946e-05 -7.84591675e-02 -8.40214908e-02 ... 3.15381766e-02\n",
|
|
|
|
|
" -1.12010893e-01 -3.72282552e-02]\n",
|
|
|
|
|
" [-4.90402044e-02 1.05809792e-01 4.49151540e-02 ... 1.60301369e-02\n",
|
|
|
|
|
" 1.08550765e-02 4.97082262e-02]\n",
|
|
|
|
|
" [-3.14740462e-03 -5.69982483e-02 6.10231506e-02 ... -9.82526432e-03\n",
|
|
|
|
|
" -1.00127875e-02 -2.95810917e-02]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [ 7.44480556e-02 -8.96343933e-02 8.58785577e-02 ... 8.83387786e-02\n",
|
|
|
|
|
" 1.47955664e-02 3.44082262e-02]\n",
|
|
|
|
|
" [-2.24684582e-02 -4.73644174e-02 1.19620978e-02 ... 1.70987684e-02\n",
|
|
|
|
|
" 3.50440139e-02 -4.66940708e-02]\n",
|
|
|
|
|
" [ 3.09912631e-03 -6.89806131e-03 -7.89365570e-03 ... -3.30881538e-02\n",
|
|
|
|
|
" 2.85286642e-02 -1.70303674e-02]]\n",
|
|
|
|
|
"float64\n",
|
|
|
|
|
"[[ 0.24292825 0.03875353 -0.04838829 ... 0.0454464 0.02616454\n",
|
|
|
|
|
" 0.07085865]\n",
|
|
|
|
|
" [-0.14605033 -0.10255992 -0.0691534 ... -0.14221534 0.05441646\n",
|
|
|
|
|
" 0.14363908]\n",
|
|
|
|
|
" [ 0.01758933 -0.09676504 -0.07188088 ... 0.01573355 -0.0923651\n",
|
|
|
|
|
" 0.04113277]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [ 0.0312834 -0.10207391 0.04388697 ... -0.07270399 0.09358967\n",
|
|
|
|
|
" 0.09282296]\n",
|
|
|
|
|
" [-0.06778366 -0.08155462 0.07426104 ... 0.00879056 0.00224737\n",
|
|
|
|
|
" -0.04201017]\n",
|
|
|
|
|
" [-0.01397604 0.0525786 0.10673341 ... -0.09476817 0.04172221\n",
|
|
|
|
|
" -0.01653008]]\n",
|
|
|
|
|
"float64\n",
|
|
|
|
|
"[[ 0.23821056 -0.22684595 -0.23900313 -0.22307994 -0.20820359 0.21907111\n",
|
|
|
|
|
" 0.2448129 0.24406478 -0.22086667 0.23578465 0.23068728 0.21603837\n",
|
|
|
|
|
" -0.22015416 0.2273947 -0.23359408 0.24316205 -0.22599274 0.2448828\n",
|
|
|
|
|
" -0.24577126 -0.24073814 0.23409303 -0.23673416 -0.23429848 0.23798849\n",
|
|
|
|
|
" 0.23932448 0.21756492 0.24914489 -0.22336549 -0.23481061 0.22419405]\n",
|
|
|
|
|
" [-0.27508135 0.32318242 -0.19603446 -0.17095617 0.28092267 -0.22193898\n",
|
|
|
|
|
" 0.2064782 0.21149604 0.24927926 -0.18680937 0.18140982 -0.39328247\n",
|
|
|
|
|
" 0.19041642 0.29306423 0.18480645 -0.16989218 0.17698483 0.22529679\n",
|
|
|
|
|
" 0.28864704 0.29179007 0.18312231 0.11926081 0.19092834 -0.36227006\n",
|
|
|
|
|
" 0.20152464 -0.36845687 0.21825649 -0.18537252 -0.17231761 0.30279298]\n",
|
|
|
|
|
" [ 0.46419226 -0.29144274 -0.21979831 -0.05094548 -0.31265563 0.47694825\n",
|
|
|
|
|
" 0.21698027 0.20500537 -0.3472583 -0.23602106 0.22101996 -0.48007579\n",
|
|
|
|
|
" 0.26360475 -0.3382583 0.28820103 -0.23432914 0.22947567 -0.32274213\n",
|
|
|
|
|
" 0.29304505 -0.33211153 0.04733828 0.29491651 0.23421252 0.31340982\n",
|
|
|
|
|
" 0.20929533 -0.42883819 0.19781346 -0.04543647 -0.04600597 -0.33294682]\n",
|
|
|
|
|
" [-0.31456648 0.20993413 0.19555102 0.20091824 -0.34795958 0.26313773\n",
|
|
|
|
|
" -0.19932059 -0.17993067 -0.52906611 0.1240308 -0.20147313 0.24848163\n",
|
|
|
|
|
" -0.10781514 0.40899696 -0.25332736 0.12730197 -0.12683918 0.1279312\n",
|
|
|
|
|
" -0.33570855 0.19567551 -0.20399474 -0.27108015 -0.11943421 -0.18252667\n",
|
|
|
|
|
" -0.1943781 0.27768788 -0.1779427 0.20619189 0.19658291 0.24801077]\n",
|
|
|
|
|
" [ 0.28003775 -0.24656041 0.28472025 -0.25652722 -0.2377857 -0.23329018\n",
|
|
|
|
|
" -0.27871007 -0.27570175 0.26014691 -0.22671371 -0.29918768 0.24169636\n",
|
|
|
|
|
" -0.28768855 -0.26286965 -0.31427227 -0.23271245 0.22844887 -0.24899349\n",
|
|
|
|
|
" -0.27570961 -0.25358862 0.25124158 0.2363703 0.21089628 0.23576767\n",
|
|
|
|
|
" -0.26226596 0.26027226 -0.28453235 -0.22935687 -0.24720796 -0.22933093]\n",
|
|
|
|
|
" [-0.48565335 -0.2465881 -0.25622676 -0.04974842 -0.58824049 -0.4359546\n",
|
|
|
|
|
" 0.26816899 0.27414157 0.43796671 -0.15452496 0.24515537 0.48205664\n",
|
|
|
|
|
" 0.25450998 -0.54418601 0.26754159 -0.16829142 0.16721371 -0.23252322\n",
|
|
|
|
|
" 0.50158931 -0.56668704 0.04247801 0.15834625 0.1594734 0.24067279\n",
|
|
|
|
|
" 0.2675666 0.48407097 0.25866951 -0.02778181 -0.04470741 -0.27621571]\n",
|
|
|
|
|
" [-0.27267255 -0.45886523 -0.17510706 -0.10178899 0.53651062 -0.22984801\n",
|
|
|
|
|
" 0.17242844 0.18302742 0.24916311 -0.15973133 0.16812989 0.42202693\n",
|
|
|
|
|
" 0.20222694 0.57612452 0.20409242 -0.18444391 0.18391435 -0.41807628\n",
|
|
|
|
|
" -0.14789935 0.52621514 0.1074553 0.20447943 0.15627734 0.44518614\n",
|
|
|
|
|
" 0.18899705 0.43286293 0.18514295 -0.09809536 -0.08563168 -0.41710236]\n",
|
|
|
|
|
" [-0.37013803 0.1514176 0.14143475 0.14731253 0.59306704 0.37691993\n",
|
|
|
|
|
" -0.14372632 -0.14582787 0.35884516 0.1232747 -0.16096911 -0.6223161\n",
|
|
|
|
|
" -0.10444428 -0.58816294 -0.36665509 0.13995272 -0.16113298 0.1199828\n",
|
|
|
|
|
" 0.26052948 0.15022914 -0.16074082 -0.36866975 -0.16849907 -0.15080841\n",
|
|
|
|
|
" -0.14371455 0.34354818 -0.14782671 0.15418654 0.15257981 0.35531016]\n",
|
|
|
|
|
" [ 0.35711621 0.17086082 0.16602409 0.18400076 0.33281758 -0.34217298\n",
|
|
|
|
|
" -0.1747656 -0.17333453 -0.36443979 0.19588652 -0.19034869 -0.33569957\n",
|
|
|
|
|
" -0.1910823 -0.36717338 0.37378397 0.17310205 -0.16627789 0.13600294\n",
|
|
|
|
|
" -0.30896351 0.19230081 -0.18328413 0.38301521 -0.16365957 -0.16838064\n",
|
|
|
|
|
" -0.17065042 -0.35753259 -0.15901984 0.18156503 0.16853124 -0.35185653]]\n",
|
|
|
|
|
"float64\n",
|
|
|
|
|
"[1800, 600, 300, 30, 9]\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"print(mlp.get_expected_output())\n",
|
|
|
|
|
"o = mlp.get_output()#[:,:5]\n",
|
|
|
|
|
"print(\"out={}\".format(o))\n",
|
|
|
|
|
"print(\"alleq? =\",all_axis_equal(o))\n",
|
|
|
|
|
"print(\"eq? =\",axis_equal(o))\n",
|
|
|
|
|
"h = output_to_hard(o)\n",
|
|
|
|
|
"print(\"hout={}\".format(h))\n",
|
|
|
|
|
"print(\"alleq? =\",all_axis_equal(h))\n",
|
|
|
|
|
"e = mlp.get_expected_output()#[:,:5]\n",
|
|
|
|
|
"print(\"expout={}\".format(e))\n",
|
|
|
|
|
"print(\"nb errors={}\".format(count_errors(h, e)))\n",
|
|
|
|
|
"#print(mlp.get_weights())\n",
|
|
|
|
|
"i = mlp.get_input()\n",
|
|
|
|
|
"print(\"ieq? =\",axis_equal(i))\n",
|
|
|
|
|
"print(\"i? =\",i)\n",
|
|
|
|
|
"print(\"diff exp=\", np.sum(e,axis=1))\n",
|
|
|
|
|
"W=mlp.get_weights()\n",
|
|
|
|
|
"ln=[]\n",
|
|
|
|
|
"for w in W:\n",
|
|
|
|
|
" print(w)\n",
|
|
|
|
|
" ln.append(len(w))\n",
|
|
|
|
|
" print(w.dtype)\n",
|
|
|
|
|
"print(ln)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 7,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"[540000, 1080000, 180000, 9000, 270]\n",
|
|
|
|
|
"14474184\n",
|
|
|
|
|
"ok\n",
|
|
|
|
|
"[1800, 600, 300, 30, 9]\n",
|
|
|
|
|
"21936\n",
|
|
|
|
|
"ok\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"True"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 7,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"import struct\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"def save_params(params, name):\n",
|
|
|
|
|
" ret = False\n",
|
|
|
|
|
" nb = len(params)\n",
|
|
|
|
|
" n_params = [p.size for p in params]\n",
|
|
|
|
|
" fmt=\"<I\" + \"I\"*nb + \"\".join([\"d\"*n for n in n_params])\n",
|
|
|
|
|
" data = [nb] + n_params\n",
|
|
|
|
|
" for p in params:\n",
|
|
|
|
|
" data += p.flatten().tolist()\n",
|
|
|
|
|
" print(n_params)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" raw = struct.pack(fmt, *data)\n",
|
|
|
|
|
" print(len(raw))\n",
|
|
|
|
|
" with open(name, \"wb\") as f:\n",
|
|
|
|
|
" f.write(raw)\n",
|
|
|
|
|
" ret = True\n",
|
|
|
|
|
" print(\"ok\")\n",
|
|
|
|
|
" return ret\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"W=mlp.get_weights()\n",
|
|
|
|
|
"err = count_errors(h, e)\n",
|
|
|
|
|
"save_params(W, \"weights_\"+str(err)+\"-577.params\")\n",
|
|
|
|
|
"b=mlp.get_bias()\n",
|
|
|
|
|
"b = mlp._b[1:]\n",
|
|
|
|
|
"save_params(b, \"bias_\"+str(err)+\"-577.params\")\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 23,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"6\n"
|
2018-01-01 20:46:31 +01:00
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2018-01-15 23:01:56 +01:00
|
|
|
"b = mlp._b\n",
|
|
|
|
|
"print(len(b))"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 29,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
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"image/png": "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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f5168cd7978>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'iterations': 1000, 'cost_function': 0.031513758450649069}\n",
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"[[ 0.01084562 0.98057713 0.98059074 0.02515126]\n",
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" [ 0.98915438 0.01942287 0.01940926 0.97484874]]\n",
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"[array([[ 1.23841713, 1.2391942 ],\n",
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" [-1.98703322, 2.42462157],\n",
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" [ 2.42389936, -1.98229619]]), array([[ 0.94829548, -2.58994826, -2.57924959],\n",
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" [-1.13555925, 2.52750395, 2.53819839]])]\n"
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]
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}
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],
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"source": [
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"from mlp import MultiLayerPerceptron\n",
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"import numpy as np\n",
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2018-01-01 20:46:31 +01:00
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"#mlp = MultiLayerPerceptron(L=1, n=[2, 1], g=[\"sigmoid\"], alpha=0.1)\n",
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"\n",
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"X = np.array([[0, 0],\n",
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" [0, 1],\n",
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" [1, 0],\n",
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" [1, 1]])\n",
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"\n",
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2018-01-15 23:01:56 +01:00
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"Y = np.array([[0, 1],\n",
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" [1, 0],\n",
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" [1, 0],\n",
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" [0, 1]])\n",
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"#Y = np.array([[0],\n",
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"# [1],\n",
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"# [1],\n",
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"# [0]])\n",
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2018-01-01 20:46:31 +01:00
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"\n",
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2018-01-15 23:01:56 +01:00
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|
|
"for i in range(15):\n",
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" mlp = MultiLayerPerceptron(L=2, n=[2, 3, 2], g=[\"tanh\", \"tanh\"], alpha=1, w_rand_factor=1)\n",
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" mlp.use_regularization(lambd=0.01)\n",
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" #mlp.use_rmsprop()\n",
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" res = mlp.learning(X.T, Y.T, 4, max_iter=1000, plot=True)\n",
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" print(res)\n",
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" print(mlp.get_output())\n",
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" print(mlp.get_weights())\n",
|
2018-01-01 20:46:31 +01:00
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"#mlp.set_all_training_examples(X.T, Y.T, 4)\n",
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"#mlp.prepare()\n",
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"#print(mlp.propagate())\n",
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"#for i in range(100):\n",
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"# print(mlp.back_propagation())\n",
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"# mlp.propagate()\n",
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"#print(mlp.propagate())\n"
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]
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},
|
2018-01-15 23:01:56 +01:00
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
|
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"text": [
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|
"[[ 0.06521739]\n",
|
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|
|
" [ 0.02173913]\n",
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|
" [ 0.89130435]\n",
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|
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" [ 0.02173913]]\n",
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"1.0\n"
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]
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|
}
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|
],
|
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|
"source": [
|
|
|
|
|
"import numpy as np\n",
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|
"\n",
|
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|
|
"a = np.ones((4,1))\n",
|
|
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|
|
"a[0] = 3\n",
|
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|
|
|
"a[2] = 41\n",
|
|
|
|
|
"res = np.zeros((4,1))\n",
|
|
|
|
|
"res = a / np.sum(a)\n",
|
|
|
|
|
"print(res)\n",
|
|
|
|
|
"print(np.sum(res))"
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|
]
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|
},
|
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
|
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"data": {
|
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|
"text/plain": [
|
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|
"0.0048747400000000007"
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]
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},
|
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|
"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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|
"source": [
|
|
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|
|
"np.sum(np.array([ 0.00015156, 0.00015268, 0.00015293, 0.00015146, 0.0001536, 0.0001529,\n",
|
|
|
|
|
" 0.0001529, 0.0001523, 0.00014965, 0.0001527, 0.00015024, 0.00015229,\n",
|
|
|
|
|
" 0.00015458 ,0.00015043 ,0.00015286, 0.00015533, 0.00015488, 0.00015392,\n",
|
|
|
|
|
" 0.00015377 , 0.0001549 , 0.0001537 , 0.00015145 , 0.00014796 , 0.00014766,\n",
|
|
|
|
|
" 0.00014974 , 0.00015326 ,0.00014838, 0.00015275 , 0.00015331, 0.00015493,\n",
|
|
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|
|
" 0.0001541 , 0.00015162]))"
|
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]
|
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|
},
|
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|
|
{
|
|
|
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|
"cell_type": "code",
|
|
|
|
|
"execution_count": 16,
|
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|
|
"metadata": {},
|
|
|
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|
"outputs": [
|
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|
{
|
|
|
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|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"[[ 1 -2 3 4]\n",
|
|
|
|
|
" [ 5 6 -7 8]]\n",
|
|
|
|
|
"[[1 0 3 4]\n",
|
|
|
|
|
" [5 6 0 8]]\n",
|
|
|
|
|
"[ 6 4 -4 12]\n",
|
|
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|
|
"[ 6 4 -4 12]\n",
|
|
|
|
|
"n= False\n",
|
|
|
|
|
"[ True True True True]\n"
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|
]
|
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|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"a=np.array([[1,-2,3,4],[5,6,-7,8]])\n",
|
|
|
|
|
"b = np.array(a)\n",
|
|
|
|
|
"a[:,1:].T\n",
|
|
|
|
|
"b\n",
|
|
|
|
|
"print(a)\n",
|
|
|
|
|
"print(np.maximum(a,0))\n",
|
|
|
|
|
"print(np.sum(a,axis=0))\n",
|
|
|
|
|
"s=np.sum(a,axis=0)\n",
|
|
|
|
|
"print(\"n=\",all_axis_equal(a))\n",
|
|
|
|
|
"a[a>0]=1\n",
|
|
|
|
|
"print(np.all(a,axis=0))"
|
|
|
|
|
]
|
|
|
|
|
},
|
2018-01-01 20:46:31 +01:00
|
|
|
{
|
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|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
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|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
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|
|
"source": []
|
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|
}
|
|
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|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": "Python 3",
|
|
|
|
|
"language": "python",
|
|
|
|
|
"name": "python3"
|
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|
|
},
|
|
|
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|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
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|
"version": 3
|
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|
},
|
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|
"file_extension": ".py",
|
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|
"mimetype": "text/x-python",
|
|
|
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|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
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|
|
"pygments_lexer": "ipython3",
|
|
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|
|
"version": "3.5.2"
|
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|
|
}
|
|
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},
|
2018-01-01 20:33:05 +01:00
|
|
|
"nbformat": 4,
|
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|
"nbformat_minor": 2
|
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|
}
|
