diff --git a/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb b/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb index 389a2b970..a556e6a42 100644 --- a/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb +++ b/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 14, "metadata": { "collapsed": true }, @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 15, "metadata": { "collapsed": true }, @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -213,34 +213,34 @@ "text": [ "start...\n", "\n", - "Minbatch=5 Cross-entropy from full softmax = 3.823 perplexity = 45.729 samples/s = 4018.9\n", - " Minibatch[ 1- 10]: loss = 2.330944 * 1000, metric = 77.8% * 1000\n", + "Minbatch=5 Cross-entropy from full softmax = 3.840 perplexity = 46.514 samples/s = 4242.3\n", + " Minibatch[ 1- 10]: loss = 2.317583 * 1000, metric = 79.3% * 1000\n", "\n", - "Minbatch=15 Cross-entropy from full softmax = 3.470 perplexity = 32.131 samples/s = 4027.7\n", - " Minibatch[ 11- 20]: loss = 2.030024 * 1000, metric = 56.1% * 1000\n", + "Minbatch=15 Cross-entropy from full softmax = 3.480 perplexity = 32.455 samples/s = 4309.5\n", + " Minibatch[ 11- 20]: loss = 2.020929 * 1000, metric = 63.9% * 1000\n", "\n", - "Minbatch=25 Cross-entropy from full softmax = 2.993 perplexity = 19.952 samples/s = 4459.7\n", - " Minibatch[ 21- 30]: loss = 1.649634 * 1000, metric = 27.2% * 1000\n", + "Minbatch=25 Cross-entropy from full softmax = 3.022 perplexity = 20.531 samples/s = 4319.0\n", + " Minibatch[ 21- 30]: loss = 1.657403 * 1000, metric = 38.4% * 1000\n", "\n", - "Minbatch=35 Cross-entropy from full softmax = 2.709 perplexity = 15.020 samples/s = 4545.3\n", - " Minibatch[ 31- 40]: loss = 1.392939 * 1000, metric = 19.7% * 1000\n", + "Minbatch=35 Cross-entropy from full softmax = 2.724 perplexity = 15.248 samples/s = 4347.4\n", + " Minibatch[ 31- 40]: loss = 1.388039 * 1000, metric = 17.5% * 1000\n", "\n", - "Minbatch=45 Cross-entropy from full softmax = 2.355 perplexity = 10.533 samples/s = 4006.5\n", - " Minibatch[ 41- 50]: loss = 1.243978 * 1000, metric = 15.1% * 1000\n", + "Minbatch=45 Cross-entropy from full softmax = 2.361 perplexity = 10.600 samples/s = 4259.8\n", + " Minibatch[ 41- 50]: loss = 1.203155 * 1000, metric = 12.9% * 1000\n", "\n", - "Minbatch=55 Cross-entropy from full softmax = 2.130 perplexity = 8.415 samples/s = 4540.4\n", - " Minibatch[ 51- 60]: loss = 0.989130 * 1000, metric = 7.8% * 1000\n", + "Minbatch=55 Cross-entropy from full softmax = 2.152 perplexity = 8.599 samples/s = 4227.4\n", + " Minibatch[ 51- 60]: loss = 1.002931 * 1000, metric = 7.7% * 1000\n", "\n", - "Minbatch=65 Cross-entropy from full softmax = 1.912 perplexity = 6.768 samples/s = 4565.8\n", - " Minibatch[ 61- 70]: loss = 0.868899 * 1000, metric = 3.8% * 1000\n", + "Minbatch=65 Cross-entropy from full softmax = 1.932 perplexity = 6.903 samples/s = 4348.0\n", + " Minibatch[ 61- 70]: loss = 0.924471 * 1000, metric = 3.9% * 1000\n", "\n", - "Minbatch=75 Cross-entropy from full softmax = 1.754 perplexity = 5.776 samples/s = 4589.9\n", - " Minibatch[ 71- 80]: loss = 0.802362 * 1000, metric = 6.9% * 1000\n", + "Minbatch=75 Cross-entropy from full softmax = 1.744 perplexity = 5.722 samples/s = 4293.9\n", + " Minibatch[ 71- 80]: loss = 0.813717 * 1000, metric = 4.9% * 1000\n", "\n", - "Minbatch=85 Cross-entropy from full softmax = 1.558 perplexity = 4.751 samples/s = 4600.2\n", - " Minibatch[ 81- 90]: loss = 0.696225 * 1000, metric = 4.3% * 1000\n", + "Minbatch=85 Cross-entropy from full softmax = 1.581 perplexity = 4.862 samples/s = 4142.6\n", + " Minibatch[ 81- 90]: loss = 0.736143 * 1000, metric = 2.8% * 1000\n", "\n", - "Minbatch=95 Cross-entropy from full softmax = 1.442 perplexity = 4.230 samples/s = 3861.5\n", + "Minbatch=95 Cross-entropy from full softmax = 1.438 perplexity = 4.210 samples/s = 3718.6\n", "done.\n" ] } @@ -271,8 +271,9 @@ " num_classes = 50\n", " hidden_dim = 10\n", " \n", - " data_sampling_distribution = lambda: np.repeat(1.0 / Param.num_classes, Param.num_classes)\n", - " softmax_sampling_weights = lambda: np.repeat(1.0 / Param.num_classes, Param.num_classes)\n", + "data_sampling_distribution = lambda: np.repeat(1.0 / Param.num_classes, Param.num_classes)\n", + " \n", + "softmax_sampling_weights = lambda: np.repeat(1.0 / Param.num_classes, Param.num_classes)\n", "\n", "\n", "# Creates random one-hot vectors of dimension 'num_classes'.\n", @@ -281,7 +282,7 @@ " indices = np.random.choice(\n", " range(Param.num_classes),\n", " size=num_vectors, \n", - " p=Param.data_sampling_distribution()).reshape((1, num_vectors))\n", + " p = data_sampling_distribution()).reshape((1, num_vectors))\n", " list_of_vectors = C.one_hot(indices, Param.num_classes)\n", " return (list_of_vectors, indices.flatten())\n", "\n", @@ -297,7 +298,7 @@ " \n", " # Connect the latent output to (sampled/full) softmax.\n", " if Param.use_sampled_softmax:\n", - " sampling_weights = np.asarray(Param.softmax_sampling_weights(), dtype=np.float32)\n", + " sampling_weights = np.asarray(softmax_sampling_weights(), dtype=np.float32)\n", " sampling_weights.reshape((1, Param.num_classes))\n", " softmax_input, ce, errs = cross_entropy_with_sampled_softmax_and_embedding(\n", " h, \n", @@ -305,7 +306,7 @@ " Param.num_classes, \n", " Param.hidden_dim, \n", " Param.softmax_sample_size, \n", - " Param.softmax_sampling_weights(),\n", + " softmax_sampling_weights(),\n", " Param.allow_duplicates)\n", " else:\n", " softmax_input, ce, errs = cross_entropy_with_softmax_and_embedding(\n", @@ -423,7 +424,7 @@ "Often the we don't have uniform distribution for the classes on the output side. The typical example is when we have words as output classes. A typical example are words where e.g. 'the' will be much more frequent than most others.\n", "\n", "In such cases one often uses a non uniform distribution for drawing the samples in sampled softmax but instead increases the sampling weight for the frequent classes. This is also called importane sampling.\n", - "In our example the sampling distribution is controlled by the weight array `Param.softmax_sampling_weights`.\n", + "In our example the sampling distribution is controlled by the weight array `softmax_sampling_weights`.\n", "\n", "As an example let's look at the case where the classes are distrubted according to zipf-distrubtion like:\n", "$$\n", @@ -441,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -455,9 +456,9 @@ }, { "data": { - "image/png": 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7XHstfP013HMPLFkCvXrBTTclNV4RkahUSeAz5wPnuPvkuHNPmNkHwFXA2GQE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\nfV4/M/vYzH41s4fNrGbsvcVm9lOsTS3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atoWXX4ZGjUIYmT273M+KiIisTyqPZr43s7vMrHfk1UhW+uUX6NkTbrwxaefaMNKgQQgj\nmoFVRERSkEoQOQJoDryUWHDuXDNrF3FdkkUaN4aBA2H0aHjxxaQ32rULYaRePejXD+bMialCERGp\nqaocRNz9MXc/ENiEMILlcOBLM3vKzA42s9j7nUj0rroK+vcP08B//nnSG5tsEsJI3bqwzz6wenVs\nNYqISM2T8qgZd5/v7te6e1fgdMIoloeBeWZ2qZk1iqpIid/aaeBbtgzTwC9enPRm+/ah0+pNN2lK\neBERqZKUg4iZtTGzs83sI+BKQgjpD5wBHAw8Fk2Jki022ihMAz93LhxxBJSUJL3Zvn1YOU9ERKQK\nUhk1c7CZPQl8TXgsMw7YxN2PcPeX3f1e4ACgX6SVSlbo0gWKiuCpp+DCC+OuRkREarpUWkT+BcwD\nern7ju5+o7svLHXMPOCyalcnWWnffUOfkRdfhJUVru4jIiKyfqk80G/r7svKO8DdlwOXpFaS1ARn\nngmjRoUBMyIiIqmqchBx92VmVhs4CNgmsXsm8FhiFV3JA2ZVCCFffw21aoURNiIiIkmqHETMbDvg\nSaAN8Eli9znAfDPb390/iLA+yQUjRoQerpMmhblHREREElLpI3IH8AHQ3t0L3L2AsCjdDOC2KIuT\nHHHHHbB8eZiB9dtv465GRESySCpBZEfgPHf/ee2OxJ//AnSPqjDJIVtsEVpDfvlFYURERNaRShCZ\nRXgsU1prQOvCCwsWlLFzyy1DGFm6NMw38t13mS5LRESyUCpB5DzgBjP7g5m1T2x/AK4HzjGzJmu3\naEuVmmD8eOjcGT4rK5JutVWYDn7x4hBGvv8+4/WJiEh2SSWIPAVsCzwEfJnYHgK2J3Ri/RlYmPiv\n5Jn99lvPNPBrbb11CCMLF8J552W8PhERyS6pzCOyZ+RVSM5o1ixMA9+zJwwfDo89BrVrlzqoUyd4\n9VVo3TqWGkVEJHukMo/IK+koRHJH585hgbwhQ8I08JdfXsZBW26Z8bpERCT7pLRUqpk1A47h9wnN\nPgTucvdFURUmNds++8DVV8NZZ8EOO0BhYdwViYhINkpl0budgM+B0UDzxHY68LmZFURbntRkZ5wR\nVukdORKmTo27GhERyUapdFa9DngC6OjuB7v7wcDmhE6s10dZnNRsZnD77bDLLvDVV3FXIyIi2SiV\nRzM7Acclryvj7qvN7GpAv/fKOho0CINkzKrwoQcfhIEDoXnztNUlIiLZIZUWkcXApmXs7wAsqV45\nkouqFEIWLoRTToEBA+Cnn9JWk4iIZIdUgsiDwJ1m9kcz65DYDiOsQVMUbXmSd5o1g4kTw7OcgQPh\nZ01HIyKSy1J5NHMm4MA9SZ9fBdwMnBtRXZLPunaFF1+E/v1DGJk4MQQUERHJOVVuEXH3X919FLAR\nYQG8HYHm7j7a3VdGXaDkqW7dQgD54osQRhYujLsiERFJgyoFETOra2arzWx7d1/m7u8ntmXpKlBy\n13ffwaLyZp7ZcccQRj7/HAYNquBgERGpiaoURNx9FfAVUHrSbpEqKSkJDR3Dh8OaNeUc2L3772Hk\nxRczVp+IiGRGKp1VLwMuNzONrZSU1aoF//gHPPss/OUvFRxcUBCW8z344IzUJiIimZNKZ9WTga2A\neWb2JfBL8pvurtlVpVIGDw7TwJ95Zuifevjh5Ry80UYZq0tERDInlSDyOGHUjEi1nX46zJgBxxwT\nFuXdaae4KxIRkUxKZfXdi9NQh+QpM7j1VvjkEzjwQHjnHWjbNu6qREQkU1JZ9G62mbUoY38zM5sd\nTVmSTxo0gEcfBXc46CBYsaKKJ3j1VbjoIvjxx7TUJyIi6ZNKZ9WOlD1qpj7QvlrVSN5q1w4eeww2\n3RRWrarih2fOhGuugc02Cx1O5s1LS40iIhK9SgcRMxtqZkMTLwevfZ3YDgIuBL5IS5WSF3beGR56\nCDbcsIofPP54+PJLOO00uOMO2HxzOOGEMORXRESyWlVaRB5LbA7cnfT6MeABYCBwRtQFilRKq1bw\n97+HQHLJJfDf/4ber8OHh3VrREQkK1U6iLh7LXevRZjQrPXa14mtvrt3dven0leqSCU0bQrnngtz\n5sDYsaH3a51UBoeJiEgmpLLWzObuviAdxYhEplEjOPnkMBynXbu4qxERkfVI6VdFM+sP9AdaUyrM\nuPvICOoSiYZZ3BWIiEg5Uhm+exHwPCGItCSswpu8iUTKHSZPTtPJZ8+G+++H1avTdAERESlPKsN3\nTwSOcvdd3P1Adz8oeYu6QJEJE6BvXxg/Pg0nf/LJ0KG1c2e47TZYuTINFxERkfVJJYjUA16PqgAz\nO8/M3jazxWb2vZn918w6VfCZvmZWUmpbY2ato6pLssfgwTBiBBx7LLz9dsQnHzUKiovD3PInnhiG\n/o4ZA0uXRnwhEREpSypB5A6gvOXJqmoP4J/ALsAAoC7wvJk1rOBzDmwNbJzY2rr7DxHWJVnCDG65\nBbp3DzOvRj5fWffu8OCDYWK0ffYJo2422ywMA/7pp4gvJiIiyVLprNoAON7MBgAzgHXmwXT306ty\nMnffN/m1mR0F/AD0AF6r4OPz3X1xVa4nNdPaaeB33jmEkVdeCfsi1bkz3HlnmC5+zBi46ioYMgSa\nN4/4QiIislYqLSJdgelACbA90D1p2zGCmpoRWjsq+lXUgOlmNs/Mnjez3SO4tmSxtm3DPGUzZoTJ\nVD1da0BvummYg2TePC0HLCKSZqmsvrtnOgoBMDMDrgdec/ePyjn0W+AEYCphjZvjgElm1tPdp6er\nPonfzjuHRovhw6FLFzj//DRerFmzNJ5cREQgxXlEAMxsK2BLYLK7Lzczc6/276jjgG2BXuUd5O6z\ngFlJu940sy2B0cCIatYgWe7ww2HBghBEYrVoEcyaFdKRiIikpMpBxMxaAA8Be/J7h9HZwJ1m9rO7\np7TejJndCOwL7OHu36ZwirepIMAAjB49mqZNm66zr7CwkMLCwhQuKXE59dS4KwDuuScUMmBAaJrp\n108TqIlIjVdUVERRUdE6+xYtWpS261lVGzHM7B7CjKrHAjOBbu4+28wGA9e6+3ZVLiKEkAOAvu4+\nu6qfT5zjeWCxu/9hPe8XANOmTZtGQUFBKpcQWdeaNaEH7eWXw/TpsOuuIZDst58CiYjklOLiYnr0\n6AHQw92Lozx3Kp1VBwHnuPvcUvs/BTar6snMbBwwnDAk+Bcza5PYGiQdc7mZ3Z30epSZDTWzLc1s\nOzO7ntBCc2MKX49IamrXhmHDwjwkzzwTXg8dCt26QVGRZmsVEamEVIJIY2BZGfubA6lMS3ki0ASY\nBMxL2g5NOqYt0CHpdT1gDGH48CRgB6C/u09K4fqSg267DU44IUPTgJiF+Udeey3MRb/JJqEjy9NP\nZ+DiIiI1WypB5FXgyKTXbma1gLOBl6t6Mnev5e61y9juSTrmaHffK+n1Ne6+tbs3dvdW7t7f3dO1\nGonUQHXqwAMPhA6t996bxqG+pe2xBzz7LLz7bnhEIyIi5UoliJxNmNDsWULLxNXAB0Af4JwIaxNJ\n2ciR8PHH0L8/HHkk7LVXeJ0xO+4YHtWIiEi5qhxE3P0DoBNh1tPHCY9qHgW6u/vn0ZYnkrq2bUNX\njQkT4OuvoWtXuPBCWL487soITTQZa6YREcleKc0j4u6LgMsirkUkLQYNgvffhyuvDNuUKfDSSzEX\nNX48PPRQ6MzStm3MxYiIxCeVRzMiNU7DhmENuxkz4NJL464GaNEC3nkHttsuNNuodURE8pSCiOSV\nzp2hd++4qwD23Rc+/BAGDw4jbIYNg/nz465KRCTjFERE4tKiRWgNeeghmDQptI48+mjcVYmIZJSC\niEgSd1iyJMMXHTYstI706gWHHAKjRmW4ABGR+FQ5iJhZQzNrlPR6MzM7zcwGRVuaSObdcw906gQP\nPpjhbhtt2oTWkHvvDWOORUTyRCotIo+TmNDMzJoBbwFnAI+b2UkR1iaScXvtBbvvDocdBnvvDZ99\nlsGLm8ERR4Rp4kVE8kQqQaSAMLsqwB+A7wlrzBwJZMOaqCIp69ABHnkEnnwSPvkEtt8e/vY3WJnK\n4gUiIlKhVIJII2DtU/RBwKPuXgK8SQqL3olko/32C902Ro8Ow327dYOXq7yAgYiIVCSVIPIZcKCZ\ndQAGA88n9rcGFkdVmEjcGjeGK66A6dOhVavw1GTFipiLevZZePXVio8TEakhUgkilwL/AOYAb7n7\nG4n9g4B3I6pLJGtstx288kqYkbVBg5iLufVW6NsXzjgjS+aqFxGpnlTWmnkY2BTYCdg76a0XgdER\n1SWSVWrVgo4d466C0IHl6qvhppugoADefjvuikREqiWleUTc/Tt3f9fdS8ysiZkdCCxx90yubyqS\nf2rXhjPPhOJi2GAD2G03+Mtf1JtWRGqsVOYRecjMTk78uSEwFXgImGFmh0Rcn0iN4J7hrhvbbgtv\nvBEW0LnmGth559CZRUSkhkmlRaQPvw/fPQgwoBlh6O4FEdUlUqNMmgR9+oQpQL78MkMXrVMHLrgg\nLJ5nBh99lKELi4hEJ5Ug0hT4KfHnvYFH3H0Z8DSwdVSFidQk/fqF7hvFxaGx4uqrYdWqDF28WzeY\nOhUKCzN0QRGR6KQSRL4GdjOzxoQgsnb47kZA3IMbRWJhBgcfDDNnwgknwHnnhb6kr72WoQLq1g1F\niIjUMKkEkeuB+4C5wDxgUmJ/H+D9aMoSqZk23BCuvTY0UDRqBHvsAcceCz//HHdlIiLZKZXhu+OA\n3YCRQO/ErKoAs1EfEREAuneH11+Hm2+GyZMzvIBeWb74AkpKKj5ORCTDUh2+O9Xd/wv8Yhbag939\naXefEml1IjVY7dpw4onhcU3z5jEWsmIF9O4NAwbAnDkxFiIi8r9SCiJmdqSZvQ8sB5ab2Qwz+1O0\npYnkhtq1Yy6gQQO49174/HPYYQe47bYsaKIREQlSmUfkdOBm4Bng0MT2HHCLmWlmVZEqyshM7Xvt\nBe+/D4cdFnrT7rMPzJ2bgQuLiJQvlRaRU4CT3P0cd38isZ0N/Jkwl4iIVMFBB0H//vDii2luqGjS\nBG6/HZ55JoSS7beHe+5R64iIxCqVINIWeL2M/a8n3hORKjjuOFi4MHTh2GUXeOyxNPcr3Wcf+OAD\n2H9/GDECJkxI48VERMqXShD5jPA4prQ/Ap9WrxyR/HPIIWG474QJYcjvQQeFrhz33pvGSdE22ihc\nYPJkGDw4TRcREalYKkHkIuBSM3vOzC5MbM8l9v812vJE8oMZDBoUpoqfMgW22AKOPDI8Pfn11zRe\neI89NBGaiMSqTlU/4O6PmNkuwGjgwMTumUBPd383yuJE8tHuu8OTT8KMGfD221CvXtwViYikT5WC\niJnVAQ4HJrj7EekpSUQAunYNW6zc1WIiImlVpUcz7r4auAVokJ5yRKQq0jrgZdEi6NVLnVlFJK1S\n6SPyNtA96kJEpOpGjYKRI+GTT9Jw8uXLYYMNYO+94fjjYcmSNFxERPJdKkFkHDDGzE42s93MrGvy\nFnWBIrJ+nTrBc8/BNtvAsGFQXBzhyTfeOLSG3HIL3H9/GMpz113wzTcRXkRE8l0qQeQBYHPgBmAK\nMB14N+m/IpIhJ58c1rO75ZYQQnr0CA0YkS20ZxZmYp0xA7p0CUsJt28fhvP85z8RXEBE8l0qQWTz\nMrYtkv4rIhlUv354cvLJJ6HhYt486Ns3rHO3dGlEF9lii9D0Mn8+PPAA9OwJG24Y0clFJJ+lMnz3\ny3QUIiLVU6cOFBaG5WSefhpefjl08YhUixbwxz+GrSLffBMW3GvRIuIiRCSXpLLo3XlmdnQZ+0ea\n2TnRlCUiqTKD/faDMWNiLuSyy6BVq9B6cuGF8OqraZwqVkRqqlQezZwAfFTG/g+BE6tXjojkjAsu\ngDvvDI91xo2DPn2gZcswh/3NN8NXX8VdoYhkgVSCyMbAD2Xsn48WvROpMcaNg0sugZ9+StMF2rWD\no48OfUp++CFME3vWWbBgAZxySlgJWETyXipB5GugVxn7ewHzqleOiGTK/Plw1VWw2WYhH3z7bRov\nVrs27LxzaCV59dWQfkaNSuMFRaSmSCWI3A5cb2ZHm9lmiW0kcF3iPRGpAS66CObMgVNPDY0THTvC\niSfC7NkoCejvAAAdnUlEQVQZuHiTJuExTXmKiuDww+Huu9OckkQkTqkEkWuAOwkTm81ObP8EbnD3\nK6p6skTn17fNbLGZfW9m/zWzTpX4XD8zm2ZmK8xslpmNqOq1RfJd69ahT+mXX4bHNP/9L2y9NQwf\nDj//HHd1wKxZcNRR4TFPt26h6WbiRFixIu7KRCQiVQ4iHpwDtAJ2BboBzd390hRr2IMQZHYBBgB1\ngefNrOH6PmBmHYGngBcT1x8L3GFmA1OsQSSvNW0K554bWkhuuCF06WjSJOaiCgth6tRQzP33Q/fu\ncN99MHAgNG8ekpOI1HjmaV01q+rMrCWhM2wfd39tPcdcBezj7l2T9hUBTd193/V8pgCYNm3aNAoK\nCtJQuYiknTt88AE8/3xouhk6NO6KRPJCcXExPXr0AOjh7lEuJlH1Cc0yoBngQHl9+XcFJpbaN4HQ\nT0VE0mz16tD/1CzDFzYLa97ssEPFx371FXz9NeyyS5jtTUSyUip9RNLGzAy4HnjN3cuaq2StjYHv\nS+37HmhiZvXTVZ+IBOPHh9E2p50WBsGsWRN3RWW4774wz33LlmG62bQsUSwi1ZVtvyaMA7al7OHB\nkRg9ejRNmzZdZ19hYSGFhYXpuqRIztlxx/BU5KGHYOxYaNMmzFN2yCFhnZu6deOuEDj7bNhrr/AY\n51//Cq0op58ehhBHPve9SO4oKiqiqKhonX2LFi1K2/Wypo+Imd0I7A/s4e7lTrloZq8A09z99KR9\nRwHXuftG6/mM+oiIRKykBN58Ex59FB55JHR2bd4czj8fzjgj7uqSrFgB11wDl18e1r75xz/CejkZ\nf7YkUjOls49IVjyaSYSQA4A9KwohCW8A/UvtG5TYLyIZUqsW7L57+Hd99myYNi3MRdKhQ9yVldKg\nQVjvZubMMLFaYSG8917cVYkIWfBoxszGAYXAUOAXM2uTeGuRu69IHHM5sIm7r50r5Bbg/xKjZ+4i\nhJI/AGWOmBGR9DODgoKwZa2OHcNkKTNmQNeuFR4uIumXDS0iJwJNgEmEKeLXbocmHdMW+O13LHef\nAwwhzDsyHRgNHOPupUfSiEgWGjMG9t03rIm3YEEMBSiEiGSN2FtE3L3CMOTuR5exbzLQIy1FiUha\ndegAv/wCxx0HJ5wQOrgeckjo8NpWS2eK5JVsaBERkTxz6KHwyithCZmbbgpzkowaBZtsEkbcPvdc\nzAW++CIsXBhzESL5QUFERGLTpk1oEXn+efj++zDKtnnzmOclWbkS/vQn6NQpFFRSEmMxIrlPQURE\nskLz5jBiBDzxBAwZEmMh9euHNW4GDoSRI6FXrzAcSETSQkFERGqcK64Ii/S9805YfiZy7dqFmVlf\neSV0Ztl5ZzjpJPjxxzRcTCS/KYiISI2zdGkYcdOzZ5qnmu/TB4qL4frrwwrAnTrBww9HfBGR/KYg\nIiI1zmWXhY6uL730+1TzffqEzq4nnhjxsjJ16sCpp8KsWeFipZaIEJHqURARkRqpTh3Yc0+48UaY\nOxemTIHhw8OIm7QMeGnTJnReHTgwDScXyV+xzyMiIlJda6eaXzvdvIjUHGoREZGcYlbxWnarV2em\nFhGpmIKIiOSV77+HzTcPC/EuWRLxyW+6CY48Er77LuITi+QuBRERySu1asEBB8All4RAcuWVYRRO\nJJo1g2eeCaNrrrsOVq2K6MQiuUtBRETySqtWoYPrZ5+Fqeb/+tcQSK6+OkwZUi3Dh4fRNX/6E5x5\nJnTvDpMmRVG2SM5SEBGRvNShA4wbB59+CgcfDH/5SwgkDz5YzRM3bx4e0UydGob67rknFBbCN99E\nUrdIrlEQEZG8ttlmcOutIZAccEDIEZHo3j3Msnb33fDyy7DvvmmaBlakZtPwXRERoGNHuP32iE9a\nq1bovHrAAfDllxUP5xHJQ2oRERFJt6ZNoWvXuKsQyUoKIiIilbRyZVjjZuXKuCsRyR0KIiIilfTK\nK3DccbD11nDbbfDrrxGduKQknFwkDymIiIhU0qBB8OGH0KtXWFyvUye4444Ipgt5+mno1y8sqjd7\ndhSlitQYCiIiIlWwzTZQVATvvw+77BJaSDp3hrvuqkYg2W+/sITwu+/CttvCxRfD8uVRli2StRRE\nRERSsN12Yc6RGTOgoACOOQbuuy/Fk5nBsGHw8cdw+ulh/vltt4XHH9eQX8l5CiIiItWwww7w8MPw\n3ntw+OHVPFnjxiGEfPABdOkCBx4Y5iP55JNIahXJRgoiIiIR6NoV6tWL6GSdOoU1a555JiSd9u0j\nOrFI9tGEZiIiGeAetlqV/fXPDPbZJ2wVKSmpwolFsot+ckVEMmDChNC48dBDITdExj30JznkEBg/\nHn7+OcKTi6SfgoiISAa0bQubbgp//CN06xb6lUQSSH79FY46CubODav+tm4NAwaEhffmzo3gAiLp\npSAiIpIB3brBs8/C66+HUDJsWOiH+uij1Qwk9evDuefCW2+F4HHDDVC7Npx2WlhieOed4bvvIvs6\nRKKmICIikkG77QbPPw+vvQatWoUnKj16hGHA1bbJJnDSSeE50Pz5YTxxt26hlUQkSymIiIjEoFcv\nmDgRJk+Gdu1g440jvkCzZmE88R13VNyRNdJOKyJVoyAiIhKjPfYIM7zH1mixbFloSTnsMHjgAVi0\nKKZCJF8piIiIZLmSkjROsLpqFfzf/8Fnn0FhYXhetPfecOut8O23abqoyO8UREREstzrr4d8MHQo\nXHVV6F+yYkVEJ2/aFC64AKZOhS+/hDFjfg8n7dqFTi1a90bSSBOaiYhkuXbt4OSTYcoU+PvfYenS\nMItrjx6hr0nv3iGkmFXzQptuCqecEraffoKnngpz1zdsGMnXIVIW8zxZUMnMCoBp06ZNo6CgIO5y\nRERSsnp1GGEzZcrvW4MG8OmnMRa1Zk0YMiw5q7i4mB49egD0cPfiKM+tRzMiIjVInTphtd9TTgl9\nS7/+Gt55p+LPTZsW4eOcZPPmhedGRxwRZmlbujQNF5FcpiAiIlLDNWtW/vsLF4Z5zZo2hd13h7PO\ngsceC1ONVFvt2nDqqfD++2GWtpYtYf/9w7BhdXaVSlAQERHJcRtuGFpExoyBzTaDBx+Egw4KQ4Y7\ndYKjjw4NGylp0wYuvjj0Jfn8c7jiijAE+IQTQueW3r3TOORHcoE6q4qI5LjatcN08t27h06vAF99\n9XsfkzfegA02iOBCW2wBo0eHbcECeO45+OabCHrRSi5TEBERyUObbhq2wsLKHX///dCoUXi0U6nJ\n11q2DP1GKuIeetpuvbUCS55SEBERkQrddFOYzwRCZujV6/etS5dqZIh33w3jkLfcEvbbD4YMgT59\nwmJ+kheyoo+Ime1hZk+Y2TdmVmJmQys4vm/iuORtjZlpZScRkTSYMiXMd3b//TBoEEyfHrqBbLtt\naPx49NEUT7zNNvDkkzBgADzySDh5y5Zw8MFw553q8JoHsqVFpDEwHbgTqOyPswOdgCW/7XD/IfrS\nREQE/vdxzuLF8NZbYabXzp1TPGnDhqElZL/9wmOa998PE6k9/TQcf3xYDXDuXD22yWFZEUTc/Tng\nOQCzKv20zXf3xempSkREytOkCQwcGLaKFBVBcTH07RsG0pQ55NgMunYN2/nnw48/hv4jCiE5LSse\nzaTIgOlmNs/Mnjez3eMuSEREyjZvXggj++8PLVqEbiGnnw6PPx5mky9Tixaw667ln3jZMrj2Wvjk\nEw0TrqFqahD5FjgBOAQ4GPgamGRmO8ZalYiIlOmMM8IssJ99BrfdBtttF7qEHHhg6BJy3nkpnviD\nD0LrSZcuoRftaafBCy/AypWR1i/pkxWPZqrK3WcBs5J2vWlmWwKjgRHxVCUiIuUxC4NjttwSjjkm\n7JszB155BTbfPMWT9uwZmlReein0LXnkERg7NkyMMmBA6HsycqQe72SxrFv0zsxKgAPd/Ykqfu5q\noJe791rP+wXAtD59+tC0adN13issLKSwsoPpRUQk415/He65J/Qx6ds3TNpaptIdXn/9tXKL8chv\nioqKKCoqWmffokWLmDx5MqRh0btcCiLPA4vd/Q/reV+r74qI1FCPPRYe33z8cXi91Va/h5K+fcNo\nnjKtWgV162aszlyV86vvmlljM+uW1Mdji8TrDon3rzCzu5OOH2VmQ81sSzPbzsyuB/YEboyhfBER\nSbMDD4SZM+G77+Chh2Dw4DB0+Mgjw/o5Q4as54MVhZB586B//9Dhddas8o+VtMiWPiI7AS8T5gZx\nYExi/93ASGBjoEPS8fUSx7QDlgEzgP7uPjlTBYuISOa1aRMW+R02LLxesAAmT67GgJmFC8Msruef\nH3rUbrVV6FuydtrYjh3VvyTNsu7RTLro0YyISP769tswXLhvX+jXL0zAtk6++OWX0OH16adDspk5\nM+zfeuswNDjPw0g6H81kS4uIiIhI2syfD198Af/5D6xZE1pW+vT5vY/Jtts2ptb++4eJTiBMpvb6\n6yHB5HkISTcFERERyXldu8Kbb8LSpSFfTJoUhg2PHh36s7ZvH4YS166d+ECLFr+HkvKUlIQmlq5d\nw6Oc3r2hQ4cKPya/UxAREZG8scEGYV29QYPC62XL4I03QmvJbyGkKpYuDf1KXnghLFEMIYis7WPS\nuzfssEOKJ88PCiIiIpK3GjUKg2Yq4g7Dh0NBQWgA2XFHqFOHsODOXXeFg374ITS3vPZaWK74kUdC\nc8vHH1djVcDcpyAiIiJSgYULQ874619h+XLYcMPQ2NGvX+hjUlAAdVu3DuOMDzwwfGj5cpg6FTp1\nKv/ky5aFRJSnFEREREQqsNFGMHFimKh16tTf+5hcemkYcNO4cVj2pmPHpA81bAh77FHxyQcPhrlz\nQ7JZ+0hnu+2gVlZM9ZV2CiIiIiKVVK8e7L572M4/Pzx5KS4OT2PWO7trRc46C15+OTzOeeABWL0a\nmjWD3XYLoWTYsIpbVWowBREREZEU1a0Lu+wStoqcdVboLNu3L+y6KzRokHhj6NCwQWheefvtEEqm\nTIGrrw4hREFEREREquObb+C55+Dii8Nkrrvs8vs8Jrvtlugm0rgx7Lln2CBMelJSUv6JP/88PDPq\n0qVGznmSHw+gREREYnb//WFK+unTQ0NHixYwblyYUb5ZsxBS/kft2hWvl3PddbDttr8PH65h1CIi\nIiKSIbVqQbduYTv11NDY8dFHoePrjjtW/PkyXXFFGKlTQ4cIK4iIiIjEpFYt2H77sFXkhhvgq6/C\nkOHevUMrChDGEg8YkM4y00qPZkRERGqABQvCoJr994fmzcPcJaefDo8/Dj/9FHd1qVMQERERqQEu\nvRS+/ho+/RRuvz20ojz8cHgq07Jl2FcT6dGMiIhIDWEWlrbZais45pgw9fycOaGPye67x11dahRE\nREREaigz2HzzsNVUejQjIiIisVEQERERkdgoiIiIiEhsFEREREQkNgoiIiIiEhsFEREREYmNgoiI\niIjERkFEREREYqMgIiIiIrFREBEREZHYKIiIiIhIbBREREREJDYKIiIiIhIbBRERERGJjYKIiIiI\nxEZBRERERGKjICIiIiKxURARERGR2CiIiIiISGwURERERCQ2CiIiIiISGwURERERiY2CiIiIiMRG\nQURERERioyAiIiIiscmKIGJme5jZE2b2jZmVmNnQSnymn5lNM7MVZjbLzEZkolapmqKiorhLyDu6\n55mne555uue5IyuCCNAYmA78GfCKDjazjsBTwItAN2AscIeZDUxfiZIK/WWRebrnmad7nnm657mj\nTtwFALj7c8BzAGZmlfjIScBsdz878foTM+sNjAZeSE+VIiIiErVsaRGpql2BiaX2TQB2i6EWERER\nSVFNDSIbA9+X2vc90MTM6sdQj4iIiKQgKx7NZEgDgJkzZ8ZdR15ZtGgRxcXFcZeRV3TPM0/3PPN0\nzzMr6d/OBlGf29wr7BuaUWZWAhzo7k+Uc8wrwDR3Pz1p31HAde6+0Xo+czhwX8TlioiI5JPh7n5/\nlCesqS0ibwD7lNo3KLF/fSYAw4E5wIr0lCUiIpKTGgAdCf+WRiorWkTMrDGwFWBAMXA68DLwk7t/\nbWZXAO3cfUTi+I7A+8A44C6gP3A9sK+7l+7EKiIiIlkqW4JIX0LwKF3M3e4+0sz+BWzm7nslfaYP\ncB2wLTAXuNTd781UzSIiIlJ9WRFEREREJD/V1OG7IiIikgMURERERCQ2eRFEzOz/zOwLM1tuZm+a\n2c5x15QrzOw8M3vbzBab2fdm9l8z61TGcZea2TwzW2ZmL5jZVnHUm2vM7NzEQpHXltqv+x0xM2tn\nZvea2YLEfX3PzApKHaP7HhEzq2VmfzOz2Yn7+ZmZXVDGcbrnKarMgrMV3V8zq29mNyX+v1hiZg+b\nWeuq1JHzQcTM/giMAS4CugPvARPMrGWsheWOPYB/ArsAA4C6wPNm1nDtAWZ2DnAycDzQE/iF8D2o\nl/lyc0ciUB9P+JlO3q/7HTEzawZMAVYCg4FtgDOAn5OO0X2P1rnACYTFULsAZwNnm9nJaw/QPa+2\nchecreT9vR4YAhwC9AHaAY9UqQp3z+kNeBMYm/TaCKNszo67tlzcgJZACdA7ad88YHTS6ybAcuDQ\nuOutqRuwAfAJsBdhxNm1ut9pvd9XAq9UcIzue7T3/Eng9lL7Hgbu0T1Py/0uAYaW2lfu/U28Xgkc\nlHRM58S5elb22jndImJmdYEewItr93m4UxPRAnnp0oyQrH8CMLPNCWsDJX8PFgNvoe9BddwEPOnu\nLyXv1P1Om/2BqWb2UOIRZLGZHbv2Td33tHgd6G9mWwOYWTegF/BM4rXueRpV8v7uRJgYNfmYT4Cv\nqML3oKbOrFpZLYHalL1AXufMl5PbzMwIzXSvuftHid0bE4JJWd+DjTNYXs4ws8OAHQl/CZSm+50e\nWwAnER7zXkZopr7BzFZ6mL9I9z16VxJ+4/7YzNYQuhL8xd0fSLyve55elbm/bYBfEwFlfcdUKNeD\niGTWOMIEc73iLiRXmVl7Qtgb4O6r4q4nj9QC3nb3CxOv3zOz7YETAU2kmB5/BA4HDgM+IoTvsWY2\nzzV5ZU7J6UczwAJgDSG1JWsDfJf5cnKXmd0I7Av0c/dvk976jtAvR9+DaPQAWgHFZrbKzFYBfYFR\nZvYr4TcR3e/ofQuUXrp7JrBp4s/6OY/e1cCV7v4fd//Q3e8jzKZ9XuJ93fP0qsz9/Q6oZ2ZNyjmm\nQjkdRBK/MU4jrEUD/Pb4oD/h+aNEIBFCDgD2dPevkt9z9y8IP5DJ34MmhFE2+h5U3URgB8Jvh90S\n21RgPNDN3Wej+50OU/jfx7mdgS9BP+dp0ojwi2SyEhL/bumep1cl7+80YHWpYzoTAnp5i9CuIx8e\nzVwL/NvMpgFvA6MJP+D/jrOoXGFm44BCYCjwi5mtTc+L3H3tKsfXAxeY2WeE1Y//Rhi59HiGy63x\n3P0XQjP1b8zsF+BHd1/7G7vud/SuA6aY2XnAQ4S/jI8Fjks6Rvc9Wk8S7udc4EOggPD39x1Jx+ie\nV0OpBWcBtkh0Cv7J3b+mgvvr7ovN7E7gWjP7GVgC3ABMcfe3K11I3EOGMjQs6c+Jm7ickNJ2irum\nXNkIv6GsKWM7stRxFxOGgi0jLCO9Vdy158oGvETS8F3d77Td532BGYl7+iEwsoxjdN+ju9+NCb9I\nfkGYv+JT4BKgju55ZPe473r+Dr+rsvcXqE+YS2pBIoj8B2hdlTq06J2IiIjEJqf7iIiIiEh2UxAR\nERGR2CiIiIiISGwURERERCQ2CiIiIiISGwURERERiY2CiIiIiMRGQURERERioyAiUoOZ2ctmdm3c\ndSQzs9vM7EczW2NmXSM87xdmdmoVju+bqKH0glzJx1xkZu9GU2HVVPXrEclV+bDWjIhkiJntDRxJ\nmDr6C8K0z1HZiTDVd2VNAdq6++IKjqv29NJmVgIc6O5PVPdcIvlGQURE1mFmtQD31NZ/2Ar41t3f\nirgs3P3HKh6/Gvgh6jpEJFp6NCNSTYnHI2PN7KrEI4lvzeyipPc3M7OS5McUZtY0sa9P4nXfxOtB\nZlZsZsvMbKKZtTKzfczsIzNbZGb3mVmDUiXUMbN/mtlCM5tvZpeWqq+emf3DzOaa2VIze8PM+ia9\nP8LMfjaz/c3sQ2AF0GE9X2tfM3vLzFaY2TwzuyIRXDCzfxFW3tw08bXMXs851l5viJl9bGa/mNlD\nZtYw8d4XZvZT4p5a0ufWeZSRuMYxZvZo4hyzzGz/UrWWlPdoJunY483sq8R5HjSzDZPe28nMnk/c\n24VmNsnMuifXRWhVeaz01524p2+b2fLE5x8pdenGZnanmS02sy/NLHk1X8ysfaKenxM/W4+Z2WZJ\n7/dLfD+WJo551czK/N6JZCsFEZFoHAksBXoCZwN/NbP+Se9XtnXhIsJq0bsBmxKWnD8VOIyw+usg\n4JRSnzkKWAXsnDj2dDM7Jun9mwjL1h8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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -477,19 +478,19 @@ "def zipf_sampling_weights():\n", " return np.asarray([ zipf(i) for i in range(Param.num_classes)], dtype=np.float32)\n", "\n", - "Param.data_sampling_distribution = lambda: zipf_sampling_weights() / np.sum(zipf_sampling_weights())\n", + "data_sampling_distribution = lambda: zipf_sampling_weights() / np.sum(zipf_sampling_weights())\n", "\n", "print(\"start...\")\n", "\n", "\n", "# Train using uniform sampling (like before)\n", "np.random.seed(1)\n", - "Param.softmax_sampling_weights = lambda: np.repeat(1.0/Param.num_classes, Param.num_classes)\n", + "softmax_sampling_weights = lambda: np.repeat(1.0/Param.num_classes, Param.num_classes)\n", "minibatch_data, cross_entropy_data, _ = train(do_print_progress = False)\n", "\n", "# Train using importance sampling\n", "np.random.seed(1)\n", - "Param.softmax_sampling_weights = zipf_sampling_weights\n", + "softmax_sampling_weights = zipf_sampling_weights\n", "minibatch_data2, cross_entropy_data2, _ = train(do_print_progress = False)\n", "\n", "plt.plot(minibatch_data, cross_entropy_data, 'r--',minibatch_data, cross_entropy_data2, 'b--')\n", @@ -529,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -548,9 +549,9 @@ }, { "data": { - "image/png": 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yYmYlSfD5z6elNc88k8a87LwzrL++b8hoZuXlZMXMOuyhh+CYY9IdpldfPQ3U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\nk4FLgBOAQ8r0YSTvxnkcKTYTyvUzuw04FhhDiv8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aAmW7GiibKO5OCu7PY2ZmZq2bOzclNhdfDKefDn37Qm3tggG722wD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\n/WWampi3cTTwR1IcJwOXACcAh5Tpw0jejfM4Umw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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -580,8 +581,8 @@ " num_classes = 50000\n", " hidden_dim = 10\n", " \n", - " data_sampling_distribution = lambda: np.repeat(1.0 / Param.num_classes, Param.num_classes)\n", - " softmax_sampling_weights = lambda: np.repeat(1.0 / Param.num_classes, Param.num_classes)\n", + "data_sampling_distribution = lambda: np.repeat(1.0 / Param.num_classes, Param.num_classes)\n", + "softmax_sampling_weights = lambda: np.repeat(1.0 / Param.num_classes, Param.num_classes)\n", "\n", " \n", "sample_sizes = [5, 10, 100, 1000]\n",