From fa9ef3346079e8619dc237b5b684c529610d3f3c Mon Sep 17 00:00:00 2001 From: Matt Graham Date: Sat, 5 Nov 2016 22:19:28 +0000 Subject: [PATCH] Adding dropout and maxout notebook answers. --- notebooks/06_Dropout_and_maxout.ipynb | 208 +++++++++++++++++++++++--- 1 file changed, 189 insertions(+), 19 deletions(-) diff --git a/notebooks/06_Dropout_and_maxout.ipynb b/notebooks/06_Dropout_and_maxout.ipynb index c4dd018..ca93675 100644 --- a/notebooks/06_Dropout_and_maxout.ipynb +++ b/notebooks/06_Dropout_and_maxout.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -43,7 +43,7 @@ " Returns:\n", " Random binary mask array of specified shape.\n", " \"\"\"\n", - " raise NotImplementedError()" + " return rng.uniform(size=shape) < prob_1" ] }, { @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -127,17 +127,20 @@ "class DropoutLayer(StochasticLayer):\n", " \"\"\"Layer which stochastically drops input dimensions in its output.\"\"\"\n", " \n", - " def __init__(self, rng=None, incl_prob=0.5):\n", + " def __init__(self, rng=None, incl_prob=0.5, share_across_batch=True):\n", " \"\"\"Construct a new dropout layer.\n", " \n", " Args:\n", " rng (RandomState): Seeded random number generator.\n", " incl_prob: Scalar value in (0, 1] specifying the probability of\n", " each input dimension being included in the output.\n", + " share_across_batch: Whether to use same dropout mask across\n", + " all inputs in a batch or use per input masks.\n", " \"\"\"\n", " super(DropoutLayer, self).__init__(rng)\n", " assert incl_prob > 0. and incl_prob <= 1.\n", " self.incl_prob = incl_prob\n", + " self.share_across_batch = share_across_batch\n", " \n", " def fprop(self, inputs, stochastic=True):\n", " \"\"\"Forward propagates activations through the layer transformation.\n", @@ -154,7 +157,12 @@ " Returns:\n", " outputs: Array of layer outputs of shape (batch_size, output_dim).\n", " \"\"\"\n", - " raise NotImplementedError()\n", + " if stochastic:\n", + " mask_shape = (1,) + inputs.shape[1:] if self.share_across_batch else inputs.shape\n", + " self._mask = (rng.uniform(size=mask_shape) < self.incl_prob)\n", + " return inputs * self._mask\n", + " else:\n", + " return inputs * self.incl_prob\n", " \n", " def bprop(self, inputs, outputs, grads_wrt_outputs):\n", " \"\"\"Back propagates gradients through a layer.\n", @@ -174,7 +182,7 @@ " Array of gradients with respect to the layer inputs of shape\n", " (batch_size, input_dim).\n", " \"\"\"\n", - " raise NotImplementedError()\n", + " return grads_wrt_outputs * self._mask \n", "\n", " def __repr__(self):\n", " return 'DropoutLayer(incl_prob={0:.1f})'.format(self.incl_prob)" @@ -189,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -249,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -283,11 +291,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0:\n", + " error(train)=2.32e+00, acc(train)=1.02e-01, error(valid)=2.32e+00, acc(valid)=9.84e-02, params_penalty=0.00e+00\n", + "Epoch 5: 1.89s to complete\n", + " error(train)=4.50e-01, acc(train)=8.63e-01, error(valid)=3.95e-01, acc(valid)=8.75e-01, params_penalty=0.00e+00\n", + "Epoch 10: 2.03s to complete\n", + " error(train)=3.72e-01, acc(train)=8.86e-01, error(valid)=3.35e-01, acc(valid)=9.00e-01, params_penalty=0.00e+00\n", + "Epoch 15: 2.07s to complete\n", + " error(train)=3.33e-01, acc(train)=8.99e-01, error(valid)=3.23e-01, acc(valid)=9.04e-01, params_penalty=0.00e+00\n", + "Epoch 20: 2.10s to complete\n", + " error(train)=3.09e-01, acc(train)=9.06e-01, error(valid)=2.85e-01, acc(valid)=9.15e-01, params_penalty=0.00e+00\n", + "Epoch 25: 2.06s to complete\n", + " error(train)=2.98e-01, acc(train)=9.10e-01, error(valid)=2.87e-01, acc(valid)=9.13e-01, params_penalty=0.00e+00\n", + "Epoch 30: 2.04s to complete\n", + " error(train)=2.92e-01, acc(train)=9.10e-01, error(valid)=2.71e-01, acc(valid)=9.20e-01, params_penalty=0.00e+00\n", + "Epoch 35: 2.04s to complete\n", + " error(train)=2.83e-01, acc(train)=9.14e-01, error(valid)=2.87e-01, acc(valid)=9.15e-01, params_penalty=0.00e+00\n", + "Epoch 40: 2.04s to complete\n", + " error(train)=2.74e-01, acc(train)=9.17e-01, error(valid)=2.56e-01, acc(valid)=9.24e-01, params_penalty=0.00e+00\n", + "Epoch 45: 2.05s to complete\n", + " error(train)=2.73e-01, acc(train)=9.18e-01, error(valid)=2.64e-01, acc(valid)=9.22e-01, params_penalty=0.00e+00\n", + "Epoch 50: 2.10s to complete\n", + " error(train)=2.72e-01, acc(train)=9.18e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01, params_penalty=0.00e+00\n", + "Epoch 55: 2.11s to complete\n", + " error(train)=2.62e-01, acc(train)=9.21e-01, error(valid)=2.50e-01, acc(valid)=9.26e-01, params_penalty=0.00e+00\n", + "Epoch 60: 2.09s to complete\n", + " error(train)=2.61e-01, acc(train)=9.21e-01, error(valid)=2.48e-01, acc(valid)=9.27e-01, params_penalty=0.00e+00\n", + "Epoch 65: 2.11s to complete\n", + " error(train)=2.58e-01, acc(train)=9.22e-01, error(valid)=2.62e-01, acc(valid)=9.24e-01, params_penalty=0.00e+00\n", + "Epoch 70: 2.18s to complete\n", + " error(train)=2.53e-01, acc(train)=9.24e-01, error(valid)=2.39e-01, acc(valid)=9.29e-01, params_penalty=0.00e+00\n", + "Epoch 75: 2.05s to complete\n", + " error(train)=2.55e-01, acc(train)=9.23e-01, error(valid)=2.52e-01, acc(valid)=9.28e-01, params_penalty=0.00e+00\n", + "Epoch 80: 2.07s to complete\n", + " error(train)=2.49e-01, acc(train)=9.25e-01, error(valid)=2.52e-01, acc(valid)=9.27e-01, params_penalty=0.00e+00\n", + "Epoch 85: 2.24s to complete\n", + " error(train)=2.51e-01, acc(train)=9.26e-01, error(valid)=2.53e-01, acc(valid)=9.29e-01, params_penalty=0.00e+00\n", + "Epoch 90: 2.64s to complete\n", + " error(train)=2.45e-01, acc(train)=9.26e-01, error(valid)=2.48e-01, acc(valid)=9.27e-01, params_penalty=0.00e+00\n", + "Epoch 95: 2.31s to complete\n", + " error(train)=2.47e-01, acc(train)=9.25e-01, error(valid)=2.40e-01, acc(valid)=9.29e-01, params_penalty=0.00e+00\n", + "Epoch 100: 2.59s to complete\n", + " error(train)=2.50e-01, acc(train)=9.25e-01, error(valid)=2.30e-01, acc(valid)=9.31e-01, params_penalty=0.00e+00\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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eqSUFIiIiIskpxDpYYY/CDPQfyIIdC7gYdzHL9YyxZ2NXroR4x7xfTERERCTX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\nMAovAJ/mnFvlq3M18CJwAodCrHPOJRbTDoVYEZF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QzQ5EREREpLI6ptoxDIsexhcjvmDj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\nxIqIiIhIyFGIFREREZGQ8//yuPaH9C6vhgAAAABJRU5ErkJggg==\n", 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vggInIbYE6tGjB4sWLcLBwQEfHx/27NnDli1bsnxd/uabb/Lzzz/Tt29fXnzx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\njpTSc8BEYDVw9hb6HwksTindkFJ6KaX0OFmSmv/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Probability of input being included in output in dropout layer\n", "incl_prob = 0.5\n", @@ -400,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -420,7 +507,6 @@ " is a multiple of this pool size such that the outputs\n", " can be split in to pools with no dimensions left over.\n", " \"\"\"\n", - " assert pool_size > 0\n", " self.pool_size = pool_size\n", " \n", " def fprop(self, inputs):\n", @@ -437,7 +523,12 @@ " \"\"\"\n", " assert inputs.shape[-1] % self.pool_size == 0, (\n", " 'Last dimension of inputs must be multiple of pool size')\n", - " raise NotImplementedError()\n", + " pooled_inputs = inputs.reshape(\n", + " inputs.shape[:-1] + \n", + " (inputs.shape[-1] // self.pool_size, self.pool_size))\n", + " pool_maxes = pooled_inputs.max(-1)\n", + " self._mask = pooled_inputs == pool_maxes[..., None]\n", + " return pool_maxes\n", "\n", " def bprop(self, inputs, outputs, grads_wrt_outputs):\n", " \"\"\"Back propagates gradients through a layer.\n", @@ -456,7 +547,7 @@ " Array of gradients with respect to the layer inputs of shape\n", " (batch_size, input_dim).\n", " \"\"\"\n", - " raise NotImplementedError()\n", + " return (self._mask * grads_wrt_outputs[..., None]).reshape(inputs.shape)\n", "\n", " def __repr__(self):\n", " return 'MaxPoolingLayer(pool_size={0})'.format(self.pool_size)" @@ -471,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -515,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -549,11 +640,90 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0:\n", + " error(train)=2.45e+00, acc(train)=7.73e-02, error(valid)=2.44e+00, acc(valid)=8.09e-02, params_penalty=0.00e+00\n", + "Epoch 5: 2.64s to complete\n", + " error(train)=2.67e-02, acc(train)=9.92e-01, error(valid)=8.87e-02, acc(valid)=9.76e-01, params_penalty=0.00e+00\n", + "Epoch 10: 2.89s to complete\n", + " error(train)=8.57e-03, acc(train)=9.97e-01, error(valid)=9.43e-02, acc(valid)=9.79e-01, params_penalty=0.00e+00\n", + "Epoch 15: 2.55s to complete\n", + " error(train)=4.83e-04, acc(train)=1.00e+00, error(valid)=8.21e-02, acc(valid)=9.82e-01, params_penalty=0.00e+00\n", + "Epoch 20: 2.57s to complete\n", + " error(train)=2.45e-04, acc(train)=1.00e+00, error(valid)=8.54e-02, acc(valid)=9.82e-01, params_penalty=0.00e+00\n", + "Epoch 25: 2.58s to complete\n", + " error(train)=1.80e-04, acc(train)=1.00e+00, error(valid)=8.81e-02, acc(valid)=9.82e-01, params_penalty=0.00e+00\n", + "Epoch 30: 2.63s to complete\n", + " error(train)=1.43e-04, acc(train)=1.00e+00, error(valid)=8.99e-02, acc(valid)=9.82e-01, params_penalty=0.00e+00\n", + "Epoch 35: 2.52s to complete\n", + " error(train)=1.19e-04, acc(train)=1.00e+00, error(valid)=9.17e-02, acc(valid)=9.82e-01, params_penalty=0.00e+00\n", + "Epoch 40: 2.52s to complete\n", + " error(train)=1.01e-04, acc(train)=1.00e+00, error(valid)=9.31e-02, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 45: 2.95s to complete\n", + " error(train)=8.91e-05, acc(train)=1.00e+00, error(valid)=9.48e-02, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 50: 2.77s to complete\n", + " error(train)=7.84e-05, acc(train)=1.00e+00, error(valid)=9.58e-02, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 55: 2.51s to complete\n", + " error(train)=7.03e-05, acc(train)=1.00e+00, error(valid)=9.69e-02, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 60: 2.54s to complete\n", + " error(train)=6.38e-05, acc(train)=1.00e+00, error(valid)=9.78e-02, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 65: 2.54s to complete\n", + " error(train)=5.82e-05, acc(train)=1.00e+00, error(valid)=9.87e-02, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 70: 2.56s to complete\n", + " error(train)=5.35e-05, acc(train)=1.00e+00, error(valid)=9.94e-02, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 75: 2.68s to complete\n", + " error(train)=4.95e-05, acc(train)=1.00e+00, error(valid)=1.00e-01, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 80: 2.55s to complete\n", + " error(train)=4.60e-05, acc(train)=1.00e+00, error(valid)=1.01e-01, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 85: 2.61s to complete\n", + " error(train)=4.30e-05, acc(train)=1.00e+00, error(valid)=1.02e-01, acc(valid)=9.82e-01, params_penalty=0.00e+00\n", + "Epoch 90: 2.62s to complete\n", + " error(train)=4.02e-05, acc(train)=1.00e+00, error(valid)=1.02e-01, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 95: 2.57s to complete\n", + " error(train)=3.79e-05, acc(train)=1.00e+00, error(valid)=1.03e-01, acc(valid)=9.83e-01, params_penalty=0.00e+00\n", + "Epoch 100: 2.64s to complete\n", + " error(train)=3.57e-05, acc(train)=1.00e+00, error(valid)=1.04e-01, acc(valid)=9.83e-01, params_penalty=0.00e+00\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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U7lJFakTJ+tTATGrOujKzqlv3bQ30bRLdJBBMx/QcQ3KLZJLjk+nSogttm7XV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\nh6wsOOkkeOYZzcqKiIiItyjE1lMjRsC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tCXjsMZg/H+6/P9eVSJIk5Q9HdJuAkSPh4INh//1zXYkkSVL+cEQ3x/72N/jL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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Size of pools to take maximum over\n", "pool_size = 2\n",