diff --git a/mlp/data_providers.py b/mlp/data_providers.py index c20063b..4ff2a8f 100644 --- a/mlp/data_providers.py +++ b/mlp/data_providers.py @@ -153,7 +153,7 @@ class MNISTDataProvider(DataProvider): rng (RandomState): A seeded random number generator. """ # check a valid which_set was provided - assert which_set in ['train', 'valid', 'eval'], ( + assert which_set in ['train', 'valid', 'test'], ( 'Expected which_set to be either train, valid or eval. ' 'Got {0}'.format(which_set) ) @@ -292,3 +292,41 @@ class CCPPDataProvider(DataProvider): targets = loaded[which_set + '_targets'] super(CCPPDataProvider, self).__init__( inputs, targets, batch_size, max_num_batches, shuffle_order, rng) + + +class AugmentedMNISTDataProvider(MNISTDataProvider): + """Data provider for MNIST dataset which randomly transforms images.""" + + def __init__(self, which_set='train', batch_size=100, max_num_batches=-1, + shuffle_order=True, rng=None, transformer=None): + """Create a new augmented MNIST data provider object. + + Args: + which_set: One of 'train', 'valid' or 'test'. Determines which + portion of the MNIST data this object should provide. + batch_size (int): Number of data points to include in each batch. + max_num_batches (int): Maximum number of batches to iterate over + in an epoch. If `max_num_batches * batch_size > num_data` then + only as many batches as the data can be split into will be + used. If set to -1 all of the data will be used. + shuffle_order (bool): Whether to randomly permute the order of + the data before each epoch. + rng (RandomState): A seeded random number generator. + transformer: Function which takes an `inputs` array of shape + (batch_size, input_dim) corresponding to a batch of input + images and a `rng` random number generator object (i.e. a + call signature `transformer(inputs, rng)`) and applies a + potentiall random set of transformations to some / all of the + input images as each new batch is returned when iterating over + the data provider. + """ + super(AugmentedMNISTDataProvider, self).__init__( + which_set, batch_size, max_num_batches, shuffle_order, rng) + self.transformer = transformer + + def next(self): + """Returns next data batch or raises `StopIteration` if at end.""" + inputs_batch, targets_batch = super( + AugmentedMNISTDataProvider, self).next() + transformed_inputs_batch = self.transformer(inputs_batch, self.rng) + return transformed_inputs_batch, targets_batch diff --git a/mlp/layers.py b/mlp/layers.py index 64a7d71..c47e727 100644 --- a/mlp/layers.py +++ b/mlp/layers.py @@ -68,6 +68,13 @@ class LayerWithParameters(Layer): """ raise NotImplementedError() + def params_penalty(self): + """Returns the parameter dependent penalty term for this layer. + + If no parameter-dependent penalty terms are set this returns zero. + """ + raise NotImplementedError() + @property def params(self): """Returns a list of parameters of layer. @@ -97,7 +104,8 @@ class AffineLayer(LayerWithParameters): def __init__(self, input_dim, output_dim, weights_initialiser=init.UniformInit(-0.1, 0.1), - biases_initialiser=init.ConstantInit(0.)): + biases_initialiser=init.ConstantInit(0.), + weights_penalty=None, biases_penalty=None): """Initialises a parameterised affine layer. Args: @@ -105,11 +113,17 @@ class AffineLayer(LayerWithParameters): output_dim (int): Dimension of the layer outputs. weights_initialiser: Initialiser for the weight parameters. biases_initialiser: Initialiser for the bias parameters. + weights_penalty: Weights-dependent penalty term (regulariser) or + None if no regularisation is to be applied to the weights. + biases_penalty: Biases-dependent penalty term (regulariser) or + None if no regularisation is to be applied to the biases. """ self.input_dim = input_dim self.output_dim = output_dim self.weights = weights_initialiser((self.output_dim, self.input_dim)) self.biases = biases_initialiser(self.output_dim) + self.weights_penalty = weights_penalty + self.biases_penalty = biases_penalty def fprop(self, inputs): """Forward propagates activations through the layer transformation. @@ -123,7 +137,7 @@ class AffineLayer(LayerWithParameters): Returns: outputs: Array of layer outputs of shape (batch_size, output_dim). """ - return inputs.dot(self.weights.T) + self.biases + return self.weights.dot(inputs.T).T + self.biases def bprop(self, inputs, outputs, grads_wrt_outputs): """Back propagates gradients through a layer. @@ -159,8 +173,27 @@ class AffineLayer(LayerWithParameters): grads_wrt_weights = np.dot(grads_wrt_outputs.T, inputs) grads_wrt_biases = np.sum(grads_wrt_outputs, axis=0) + + if self.weights_penalty is not None: + grads_wrt_weights += self.weights_penalty.grad(self.weights) + + if self.biases_penalty is not None: + grads_wrt_biases += self.biases_penalty.grad(self.biases) + return [grads_wrt_weights, grads_wrt_biases] + def params_penalty(self): + """Returns the parameter dependent penalty term for this layer. + + If no parameter-dependent penalty terms are set this returns zero. + """ + params_penalty = 0 + if self.weights_penalty is not None: + params_penalty += self.weights_penalty(self.weights) + if self.biases_penalty is not None: + params_penalty += self.biases_penalty(self.biases) + return params_penalty + @property def params(self): """A list of layer parameter values: `[weights, biases]`.""" @@ -232,7 +265,9 @@ class SoftmaxLayer(Layer): Returns: outputs: Array of layer outputs of shape (batch_size, output_dim). """ - exp_inputs = np.exp(inputs) + # subtract max inside exponential to improve numerical stability - + # when we divide through by sum this term cancels + exp_inputs = np.exp(inputs - inputs.max(-1)[:, None]) return exp_inputs / exp_inputs.sum(-1)[:, None] def bprop(self, inputs, outputs, grads_wrt_outputs): @@ -258,6 +293,7 @@ class SoftmaxLayer(Layer): def __repr__(self): return 'SoftmaxLayer' + class RadialBasisFunctionLayer(Layer): """Layer implementing projection to a grid of radial basis functions.""" diff --git a/notebooks/04_Generalisation_and_overfitting.ipynb b/notebooks/04_Generalisation_and_overfitting.ipynb index 8bc0f1f..ca58f4f 100644 --- a/notebooks/04_Generalisation_and_overfitting.ipynb +++ b/notebooks/04_Generalisation_and_overfitting.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ " One dimensional array of output values of shape (num_inputs,)\n", " \n", " \"\"\"\n", - " raise NotImplementedError()" + " return (inputs[:, None]**np.arange(coefficients.shape[0])).dot(coefficients)" ] }, { @@ -91,22 +91,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "ename": "NotImplementedError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtest_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtest_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m21.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m assert polynomial_function(test_inputs, test_coefficients).shape == (4,), (\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m'Function gives wrong shape output.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m )\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mpolynomial_function\u001b[0;34m(inputs, coefficients)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \"\"\"\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNotImplementedError\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "test_coefficients = np.array([-1., 3., 4.])\n", "test_inputs = np.array([0., 0.5, 1., 2.])\n", @@ -128,10 +115,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "coefficients = np.array([0, -1., 8., -17., 10.])\n", @@ -153,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -175,9 +160,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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h1qxZHglWRORcYRYWYG7bjNErDSM5xdfhiAQclwucV155pck2Q4YM4Z133mlV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\n5cCBAyQkJPgi3IDlSp7j4uLIz88HYO/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(8, 4))\n", "ax = fig.add_subplot(111)\n", @@ -202,9 +198,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MTqxgxdqElYjgXXo9iIc/4WXXcaKeLpkHhw4g7bu5jpJvkpaBtL4Q/fBddMOP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\nAwAAAA6hXAMAAAAOoVwDAAAADvk/MW3Utrx4VB8AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "num_weights = 15\n", "weights_scale = 1.\n", @@ -226,7 +243,6 @@ "ax = fig.add_subplot(1, 1, 1)\n", "for weight, centre in zip(weights, centres):\n", " ys += weight * basis_function(xs, centre, scale)\n", - "ys += bias\n", "ax.plot(xs, ys)\n", "ax.set_xlabel('Input', fontsize=14)\n", "ax.set_ylabel('Output', fontsize=14)" @@ -245,10 +261,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "from mlp.models import MultipleLayerModel\n", @@ -278,9 +292,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "Training model with 2 weights\n", + "--------------------------------------------------------------------------------\n", + " Final training set error: 2.0e-03\n", + " Final validation set error: 1.1e-03\n", + "--------------------------------------------------------------------------------\n", + "Training model with 5 weights\n", + "--------------------------------------------------------------------------------\n", + " Final training set error: 4.5e-04\n", + " Final validation set error: 3.0e-04\n", + "--------------------------------------------------------------------------------\n", + "Training model with 10 weights\n", + "--------------------------------------------------------------------------------\n", + " Final training set error: 5.1e-05\n", + " Final validation set error: 8.3e-05\n", + "--------------------------------------------------------------------------------\n", + "Training model with 25 weights\n", + "--------------------------------------------------------------------------------\n", + " Final training set error: 3.9e-05\n", + " Final validation set error: 9.5e-05\n", + "--------------------------------------------------------------------------------\n", + "Training model with 50 weights\n", + "--------------------------------------------------------------------------------\n", + " Final training set error: 1.5e-05\n", + " Final validation set error: 1.6e-03\n", + "--------------------------------------------------------------------------------\n", + "Training model with 100 weights\n", + "--------------------------------------------------------------------------------\n", + " Final training set error: 1.0e-05\n", + " Final validation set error: 4.2e-03\n" + ] + } + ], "source": [ "num_weight_list = [2, 5, 10, 25, 50, 100]\n", "models = []\n", @@ -323,10 +374,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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grNgiCADAESwWi0pLS1VZWalAIKC8vDylpKSorq5Oqampcjqdys/PV01NjcrK\nyhQXF6fy8nJJUkpKirKzs1VRUSGz2aw5c+bIbDaro6NDK1euVCAQkGEYys7O1owZMyRJy5cv1/79\n+yVJZ599tubOnTtssQMABocECwCAPmRmZiozMzPk2A033BD8HB0drYqKij7blpSUqKSkJOTY2Wef\nrccee6zP+g899NAgZwsAOFmwRRAAAAAAwoQECwAAAADChAQLAAAAAMKEBAsAAAAAwoQECwAAAADC\nhAQLAAAAAMKEBAsAAAAAwoQECwAAAADChAQLAAAAAMKEBAsAAAAAwoQECwAAAADChAQLAAAAAMKE\nBAsAAAAAwoQECwAAAADChAQLAAAAAMKEBAsAAAAAwoQECwAAAADCxBqpgVpaWrR69WoFAgEVFBSo\nuLg45Hx3d7dqamq0a9cu2Ww2lZeXKyEhQZK0fv16NTQ0yGw2a/bs2crIyJAkrVq1Sps3b9bYsWNV\nVVUV7GvdunV6/fXXNWbMGEnSjTfeqMzMzGP2BQAAAACDFZErWIFAQLW1tVqwYIGqq6vV2Niotra2\nkDoNDQ2KjY3VihUrVFRUpLVr10qS2tra5HK5tHTpUi1cuFC1tbUKBAKSpNzcXC1YsKDPMYuKirR4\n8WItXrw4mFwdqy8AAAAAGKyIJFitra1KSkpSYmKirFarcnJy5Ha7Q+o0NzcrNzdXkpSVlaWtW7fK\nMAy53W7l5OQoKipKCQkJSkpKUmtrqyRp2rRpiouL6/c8jtUXAAAAAAxWRBIsn88nh8MRLDscDvl8\nvqPWsVgsiomJUWdnZ6+2dru9V9u+vPrqq/rRj36kVatW6cCBA33Oo799AQAAAEB/ROwerEi68sor\ndd1110mS6urq9Oyzz2revHn9bl9fX6/6+npJ0qJFixQfHz/guewZcMvhYbVaBxXvyYzYRiZiG5lG\nc2wAABxLRBIsu92u9vb2YLm9vV12u73POg6HQz09Perq6pLNZuvV1ufz9Wp7pHHjxgU/FxQU6Oc/\n/3mf8zhaX4WFhSosLAyW9+7d289IRz6/3z9q442Pjye2EYjYRqbBxpacnBzG2QAAEDkR2SKYmpoq\nj8cjr9crv98vl8slp9MZUmfGjBnasGGDJKmpqUnp6ekymUxyOp1yuVzq7u6W1+uVx+NRWlraMcfr\n6OgIfn777beVkpIiSQPqCwAAAAD6KyJXsCwWi0pLS1VZWalAIKC8vDylpKSorq5Oqampcjqdys/P\nV01Njcr7CoOsAAAgAElEQVTKyhQXF6fy8nJJUkpKirKzs1VRUSGz2aw5c+bIbP4yL1y2bJm2b9+u\nzs5O3XHHHZo1a5by8/O1Zs0affzxxzKZTJowYYLmzp173L4AAAAAYLBMhmEYwz2Jk93u3bsH3Lbn\n9mvDOJOhl7jexZalEYjYRiZiOzq2CPbPYNankYZ/LyPPaI1LIrbDTrWfc/u7NnH5BgAAAADChAQL\nAAAAAMKEBAsAAAAAwoQECwAAAADChAQLAAAAAMKEBAsAAAAAwiQi78ECAGCkaWlp0erVqxUIBFRQ\nUKDi4uKQ893d3aqpqdGuXbtks9lUXl6uhIQESdL69evV0NAgs9ms2bNnKyMjQ4cOHdJDDz0kv9+v\nnp4eZWVladasWZIkr9erZcuWqbOzU5MnT1ZZWZmsVpZoABiJuIIFAMARAoGAamtrtWDBAlVXV6ux\nsVFtbW0hdRoaGhQbG6sVK1aoqKhIa9eulSS1tbXJ5XJp6dKlWrhwoWpraxUIBBQVFaWHHnpIixcv\n1mOPPaaWlhbt2LFDkrRmzRoVFRVpxYoVio2NVUNDQ8RjBgCEBwkWAABHaG1tVVJSkhITE2W1WpWT\nkyO32x1Sp7m5Wbm5uZKkrKwsbd26VYZhyO12KycnR1FRUUpISFBSUpJaW1tlMpl02mmnSZJ6enrU\n09Mjk8kkwzC0bds2ZWVlSZJyc3N7jQUAGDnYfwAAwBF8Pp8cDkew7HA4tHPnzqPWsVgsiomJUWdn\np3w+n6ZMmRKsZ7fb5fP5JH15Zezee+/VJ598oquuukpTpkzR/v37FRMTI4vF0qv+kerr61VfXy9J\nWrRokeLj48MX9EnOarWO2nhHa2yjNS6J2A7bM8RzCbdI/b2RYAEAECFms1mLFy/WwYMHtWTJEv3j\nH//QuHHj+t2+sLBQhYWFwfLevXuHYponpfj4+FEb72iNbbTGJRHbSOX3+wcVW3Jycr/qsUUQAIAj\n2O12tbe3B8vt7e2y2+1HrdPT06Ouri7ZbLZebX0+X6+2sbGxSk9PV0tLi2w2m7q6utTT03PU+gCA\nkYMECwCAI6Smpsrj8cjr9crv98vlcsnpdIbUmTFjhjZs2CBJampqUnp6ukwmk5xOp1wul7q7u+X1\neuXxeJSWlqb9+/fr4MGDkqRDhw7p3Xff1cSJE2UymZSenq6mpiZJ0oYNG3qNBQAYOdgiCADAESwW\ni0pLS1VZWalAIKC8vDylpKSorq5Oqampcjqdys/PV01NjcrKyhQXF6fy8nJJUkpKirKzs1VRUSGz\n2aw5c+bIbDaro6NDK1euVCAQkGEYys7O1owZMyRJN998s5YtW6YXXnhBkyZNUn5+/nCGDwAYBBIs\nAAD6kJmZqczMzJBjN9xwQ/BzdHS0Kioq+mxbUlKikpKSkGNnn322HnvssT7rJyYm6tFHHx3kjAEA\nJwO2CAIAAABAmJBgAQAAAECYkGABAAAAQJiQYAEAAABAmJBgAQAAAECYkGABAAAAQJiQYAEAAABA\nmJBgAQAAAECYkGABAAAAQJiQYAEAAABAmJBgAQAAAECYkGABAAAAQJiQYAEAAABAmJBgAQAAAECY\nkGABAAAAQJiQYAEAAABAmJBgAQAAAECYkGABAAAAQJiQYAEAAABAmJBgAQAAAECYWCM1UEtLi1av\nXq1AIKCCggIVFxeHnO/u7lZNTY127dolm82m8vJyJSQkSJLWr1+vhoYGmc1mzZ49WxkZGZKkVatW\nafPmzRo7dqyqqqqCfT333HN65513ZLValZiYqHnz5ik2NlZer1fz589XcnKyJGnKlCmaO3duhL4B\nAAAAAKNdRK5gBQIB1dbWasGCBaqurlZjY6Pa2tpC6jQ0NCg2NlYrVqxQUVGR1q5dK0lqa2uTy+XS\n0qVLtXDhQtXW1ioQCEiScnNztWDBgl7jXXjhhaqqqtKSJUt0xhlnaP369cFzSUlJWrx4sRYvXkxy\nBQAAACCsIpJgtba2KikpSYmJibJarcrJyZHb7Q6p09zcrNzcXElSVlaWtm7dKsMw5Ha7lZOTo6io\nKCUkJCgpKUmtra2SpGnTpikuLq7XeBdddJEsFoskaerUqfL5fEMbIAAAAAAoQlsEfT6fHA5HsOxw\nOLRz586j1rFYLIqJiVFnZ6d8Pp+mTJkSrGe3208oYWpoaFBOTk6w7PV6dc899+j000/Xd77zHZ1/\n/vm92tTX16u+vl6StGjRIsXHx/d7vCPtGXDL4WG1WgcV78mM2EYmYhuZRnNsAAAcS8TuwRoOL7/8\nsiwWiy699FJJ0vjx47Vq1SrZbDbt2rVLixcvVlVVlWJiYkLaFRYWqrCwMFjeu3dvROc9nPx+/6iN\nNz4+nthGIGIbmQYb2+F7ZQEAGGkiskXQbrervb09WG5vb5fdbj9qnZ6eHnV1dclms/Vq6/P5erXt\ny4YNG/TOO+/ozjvvlMlkkiRFRUXJZrNJkiZPnqzExER5PJ5BxwcAAAAAUoQSrNTUVHk8Hnm9Xvn9\nfrlcLjmdzpA6M2bM0IYNGyRJTU1NSk9Pl8lkktPplMvlUnd3t7xerzwej9LS0o45XktLi37729/q\n3nvv1Ve+8pXg8f379wcfkLFnzx55PB4lJiaGN1gAAAAAp6yIbBG0WCwqLS1VZWWlAoGA8vLylJKS\norq6OqWmpsrpdCo/P181NTUqKytTXFycysvLJUkpKSnKzs5WRUWFzGaz5syZI7P5y7xw2bJl2r59\nuzo7O3XHHXdo1qxZys/PV21trfx+vx555BFJ//c49u3bt2vdunWyWCwym826/fbb+3xIBgAAAAAM\nhMkwDGO4J3Gy271794Db9tx+bRhnMvQS17u4J2QEIraRidiOjnuw+mcw69NIw7+XkWe0xiUR22Gn\n2s+5/V2bIrJFEAAAAABOBSRYAAAAABAmJFgAAAAAECaj+j1YAAAMVEtLi1avXq1AIKCCggIVFxeH\nnO/u7lZNTY127dolm82m8vJyJSQkSJLWr1+vhoYGmc1mzZ49WxkZGdq7d69Wrlypzz77TCaTSYWF\nhZo5c6Ykad26dXr99dc1ZswYSdKNN96ozMzMyAYMAAgLEiwAAI4QCARUW1urBx54QA6HQ/fff7+c\nTqfOPPPMYJ2GhgbFxsZqxYoVamxs1Nq1azV//ny1tbXJ5XJp6dKl6ujo0COPPKLHH39cFotFt956\nqyZPnqzPP/9c9913ny688MJgn0VFRbr22pF1wzgAoDe2CAIAcITW1lYlJSUpMTFRVqtVOTk5crvd\nIXWam5uVm5srScrKytLWrVtlGIbcbrdycnIUFRWlhIQEJSUlqbW1VePHj9fkyZMlSaeffromTpwo\nn88X6dAAAEOMBAsAgCP4fD45HI5g2eFw9EqG/rOOxWJRTEyMOjs7e7W12+292nq9Xn300UdKS0sL\nHnv11Vf1ox/9SKtWrdKBAweGIiwAQASwRRAAgAj64osvVFVVpdtuu00xMTGSpCuvvFLXXXedJKmu\nrk7PPvus5s2b16ttfX296uvrJUmLFi1SfHx85CY+zKxW66iNd7TGNlrjkojtsD1DPJdwi9TfGwkW\nAABHsNvtam9vD5bb29tlt9v7rONwONTT06Ouri7ZbLZebX0+X7Ct3+9XVVWVLr30Ul1yySXBOuPG\njQt+Ligo0M9//vM+51VYWKjCwsJgebS+6LQvvNh15BmtcUnENlL5/X5eNAwAwHBITU2Vx+OR1+uV\n3++Xy+WS0+kMqTNjxgxt2LBBktTU1KT09HSZTCY5nU65XC51d3fL6/XK4/EoLS1NhmHoiSee0MSJ\nE3XNNdeE9NXR0RH8/PbbbyslJWXIYwQADA2uYAEAcASLxaLS0lJVVlYqEAgoLy9PKSkpqqurU2pq\nqpxOp/Lz81VTU6OysjLFxcWpvLxckpSSkqLs7GxVVFTIbDZrzpw5MpvN+uCDD7Rp0yadddZZuvvu\nuyX93+PY16xZo48//lgmk0kTJkzQ3LlzhzN8AMAgkGABANCHzMzMXu+iuuGGG4Kfo6OjVVFR0Wfb\nkpISlZSUhBw777zztG7duj7rl5WVDXK2AICTBVsEAQAAACBMSLAAAAAAIExIsAAAAAAgTEiwAAAA\nACBMSLAAAAAAIExIsAAAAAAgTEiwAAAAACBMSLAAAAAAIExIsAAAAAAgTEiwAAAAACBM+pVgBQIB\nPfzww+ru7h7q+QAAMGisWwCA4dKvBMtsNsvr9cowjKGeDwAAg8a6BQAYLv3eInjdddfpySef1Kef\nfqpAIBDyBwCAkw3rFgBgOFj7W/EXv/iFJGnTpk29ztXV1YVvRgAAhAHrFgBgOPQ7waqpqRnKeQAA\nEFasWwCA4dDvBGvChAmSvrxxeN++fRo7dqzMZh5CCAA4ObFuAQCGQ78TrK6uLj399NNqbGxUIBCQ\nxWJRTk6OSktLFRMTM5RzBADghLFuAQCGQ79/lbd69Wp98cUXqqqq0po1a7RkyRIdOnRITz/99FDO\nDwCAAWHdAgAMh34nWC0tLSorK1NycrKioqKUnJysefPm6a9//etQzg8AgAFh3QIADId+J1jR0dHa\nv39/yLH9+/fLau33LkMAACKGdQsAMBz6vcrk5+frpz/9qYqKijRhwgR9+umn+v3vf6/CwsKhnB8A\nAAPCugUAGA79TrBKSko0fvx4NTY2yufzyW6361vf+pby8vKGcn4AAAwI6xYAYDj0K8EKBAJ68cUX\nVVJSovz8/KGeEwAAg8K6BQAYLv1KsMxms1577TVdf/31Ax6opaVFq1evViAQUEFBgYqLi0POd3d3\nq6amRrt27ZLNZlN5ebkSEhIkSevXr1dDQ4PMZrNmz56tjIwMSdKqVau0efNmjR07VlVVVcG+Dhw4\noOrqan366aeaMGGC5s+fr7i4OBmGodWrV2vLli36yle+onnz5mny5MkDjgkAcHIKx7oFAMBA9Psh\nF5dddpn+9Kc/DWiQQCCg2tpaLViwQNXV1WpsbFRbW1tInYaGBsXGxmrFihUqKirS2rVrJUltbW1y\nuVxaunSpFi5cqNraWgUCAUlSbm6uFixY0Gu8V155RdOnT9fy5cs1ffp0vfLKK5KkLVu26JNPPtHy\n5cs1d+5cPfXUUwOKBwBw8hvMugUAwED1+x6s1tZW/fGPf9Tvfvc7ORwOmUym4Lkf//jHx22blJSk\nxMRESVJOTo7cbrfOPPPMYJ3m5ubgbxqzsrL09NNPyzAMud1u5eTkKCoqSgkJCUpKSlJra6umTp2q\nadOmyev19hrP7Xbr4YcfliRdfvnlevjhh3XLLbeoublZl112mUwmk6ZOnaqDBw+qo6ND48eP7+/X\nAAAYIQazbgEAMFD9TrAKCgpUUFAwoEF8Pp8cDkew7HA4tHPnzqPWsVgsiomJUWdnp3w+n6ZMmRKs\nZ7fb5fP5jjnevn37gknTuHHjtG/fvuAY8fHxIfPw+Xy9Eqz6+nrV19dLkhYtWhTS5kTtGXDL4WG1\nWgcV78mM2EYmYhuZTobYBrNuAQAwUP1+yMWePXtUUlKiqKiooZ5TWJlMppDfWvZHYWFhyGN89+7d\nG+5pnbT8fv+ojTc+Pp7YRiBiG5kGG1tycvKgxh/J6xYAYGTr1z1Yh28WtlgsAxrEbrervb09WG5v\nb5fdbj9qnZ6eHnV1dclms/Vqe/hRu8cyduxYdXR0SJI6Ojo0ZsyY4Bj/ueD3NQ8AwMg32HULAICB\nishDLlJTU+XxeOT1euX3++VyueR0OkPqzJgxQxs2bJAkNTU1KT09XSaTSU6nUy6XS93d3fJ6vfJ4\nPEpLSzvmeE6nUxs3bpQkbdy4URdffHHw+KZNm2QYhnbs2KGYmBjuvwKAUYqHXAAAhkNEHnJhsVhU\nWlqqyspKBQIB5eXlKSUlRXV1dUpNTZXT6VR+fr5qampUVlamuLg4lZeXS5JSUlKUnZ2tiooKmc1m\nzZkzR2bzl3nhsmXLtH37dnV2duqOO+7QrFmzlJ+fr+LiYlVXV6uhoSH4mHZJ+upXv6rNmzfrzjvv\nVHR0tObNm3fCXxgAYGTgIRcAgOFgMgzD6E/Fw1eXenVgMunyyy8P55xOOrt37x5w257brw3jTIZe\n4noX94SMQMQ2MhHb0Q32Hizp1Fi3BrM+jTT8exl5RmtcErEddqr9nNvftem4WwSffvppSV++cyo3\nN1eBQCD4OTc3V263e8CTBAAg3Fi3AADD6bhbBDdu3KjS0tJg+bnnnlN+fn6w/N577w3NzAAAGIBw\nrVstLS1avXq1AoGACgoKVFxcHHK+u7tbNTU12rVrl2w2m8rLy5WQkCBJWr9+vRoaGmQ2mzV79mxl\nZGRo7969WrlypT777DOZTCYVFhZq5syZkqQDBw6ourpan376aXBre1xc3GC/CgDAMDjuFazj7SDs\n5w5DAAAiIhzrViAQUG1trRYsWKDq6mo1Njaqra0tpE5DQ4NiY2O1YsUKFRUVae3atZKktrY2uVwu\nLV26VAsXLlRtba0CgYAsFotuvfVWVVdXq7KyUq+++mqwz1deeUXTp0/X8uXLNX36dL3yyisDjB4A\nMNyOm2Ad7x1SJ/qOKQAAhlI41q3W1lYlJSUpMTFRVqtVOTk5vbYWNjc3Kzc3V5KUlZWlrVu3yjAM\nud1u5eTkKCoqSgkJCUpKSlJra6vGjx+vyZMnS5JOP/10TZw4UT6fT5LkdruD94VdfvnlbGMEgBHs\nuFsEe3p6tHXr1mA5EAj0KgMAcLIIx7rl8/nkcDiCZYfDoZ07dx61jsViUUxMjDo7O+Xz+TRlypRg\nPbvdHkykDvN6vfroo4+Crx3Zt29f8LUh48aN0759+/qcV319verr6yVJixYtUnx8/HFjGS2sVuuo\njXe0xjZa45KI7bA9QzyXcIvU39txE6yxY8fqv//7v4PluLi4kPLhl/gCAHAyONnXrS+++EJVVVW6\n7bbbFBMT0+u8yWQ66lW2wsJCFRYWBsuj9SlmfeGpbSPPaI1LIraRyu/3R+QpgsdNsFauXDngSQAA\nEGnhWLfsdrva29uD5fb2dtnt9j7rOBwO9fT0qKurSzabrVdbn88XbOv3+1VVVaVLL71Ul1xySbDO\n2LFj1dHRofHjx6ujo2PYk0AAwMAd9x4sAABONampqfJ4PPJ6vfL7/XK5XHI6nSF1ZsyYEXzXVlNT\nk9LT02UymeR0OuVyudTd3S2v1yuPx6O0tDQZhqEnnnhCEydO1DXXXBPSl9Pp1MaNGyV9+RTEiy++\nOCJxAgDC77hXsAAAONVYLBaVlpaqsrJSgUBAeXl5SklJUV1dnVJTU+V0OpWfn6+amhqVlZUpLi5O\n5eXlkqSUlBRlZ2eroqJCZrNZc+bMkdls1gcffKBNmzbprLPO0t133y1JuvHGG5WZmani4mJVV1er\noaEh+Jh2AMDIRIIFAEAfMjMzlZmZGXLshhtuCH6Ojo5WRUVFn21LSkpUUlIScuy8887TunXr+qxv\ns9n04IMPDnLGAICTAVsEAQAAACBMSLAAAAAAIExIsAAAAAAgTEiwAAAAACBMSLAAAAAAIExIsAAA\nAAAgTEiwAAAAACBMSLAAAAAAIExIsAAAAAAgTEiwAAAAACBMSLAAAAAAIExIsAAAAAAgTEiwAAAA\nACBMSLAAAAAAIExIsAAAAAAgTEiwAAAAACBMSLAAAAAAIExIsAAAAAAgTEiwAAAAACBMSLAAAAAA\nIExIsAAAAAAgTEiwAAAAACBMSLAAAAAAIExIsAAAAAAgTKyRGqilpUWrV69WIBBQQUGBiouLQ853\nd3erpqZGu3btks1mU3l5uRISEiRJ69evV0NDg8xms2bPnq2MjIxj9vnggw/q888/lyTt379fqamp\nuueee7Rt2zY99thjwX4vueQSXXfddZH6CgAAAACMchFJsAKBgGpra/XAAw/I4XDo/vvvl9Pp1Jln\nnhms09DQoNjYWK1YsUKNjY1au3at5s+fr7a2NrlcLi1dulQdHR165JFH9Pjjj0vSUfv8yU9+Eux3\nyZIluvjii4Pl888/X/fdd18kwgYAAABwiolIgtXa2qqkpCQlJiZKknJycuR2u0MSrObmZl1//fWS\npKysLD399NMyDENut1s5OTmKiopSQkKCkpKS1NraKknH7bOrq0vbtm3TvHnzIhEmAAAAEKLn9muH\newonZr1ruGcw4kXkHiyfzyeHwxEsOxwO+Xy+o9axWCyKiYlRZ2dnr7Z2u10+n69ffbrdbl1wwQWK\niYkJHtuxY4fuvvtu/exnP9M///nPsMYJAAAA4NQWsXuwhkNjY6Py8/OD5UmTJmnVqlU67bTTtHnz\nZi1evFjLly/v1a6+vl719fWSpEWLFik+Pn7Ac9gz4JbDw2q1DirekxmxjUzENjKN5tgAADiWiCRY\ndrtd7e3twXJ7e7vsdnufdRwOh3p6etTV1SWbzdarrc/nC7Y9Vp/79+9Xa2urfvSjHwWP/eeVrMzM\nTNXW1mr//v0aM2ZMyFwKCwtVWFgYLO/du3egoY84fr9/1MYbHx9PbCMQsY1Mg40tOTk5jLMBACBy\nIrJFMDU1VR6PR16vV36/Xy6XS06nM6TOjBkztGHDBklSU1OT0tPTZTKZ5HQ65XK51N3dLa/XK4/H\no7S0tOP22dTUpMzMTEVHRwePffbZZzIMQ9KX94UFAgHZbLah/wIAAAAAnBIicgXLYrGotLRUlZWV\nCgQCysvLU0pKiurq6pSamiqn06n8/HzV1NSorKxMcXFxKi8vlySlpKQoOztbFRUVMpvNmjNnjszm\nL/PCvvo8zOVy9XoUfFNTk1577TVZLBZFR0ervLxcJpMpEl8BAAAAgFNAxO7ByszMVGZmZsixG264\nIfg5OjpaFRUVfbYtKSlRSUlJv/o87OGHH+517Oqrr9bVV199ArMGAJyqhuL9jatWrdLmzZs1duxY\nVVVVBftat26dXn/99eCW9RtvvPGo6xsA4OQWkS2CAACMJIff37hgwQJVV1ersbFRbW1tIXX+8/2N\nRUVFWrt2rSSFvL9x4cKFqq2tVSAQkCTl5uZqwYIFfY5ZVFSkxYsXa/HixSRXADCCkWABAHCE/3x/\no9VqDb5r8T81NzcrNzdX0pfvb9y6detx3984bdo0xcXFRTocAEAEjerHtAMAMBB9vWtx586dR61z\n5Psbp0yZEqx3+P2Nx/Pqq69q06ZNmjx5sr773e/2mYiF8zUiI81ofvT/aI1ttMYlnVhso/mVPaM5\ntkGNM+QjAACAY7ryyit13XXXSZLq6ur07LPPat68eb3qncqvEeG1BiPPaI1LGt2xjeZX9gw2tv6+\nQoQtggAAHOFE3t8oqd/vbzyacePGyWw2y2w2q6CgQH/729/CGA0AIJJIsAAAOMJQvL/xWDo6OoKf\n33777ZDXjgAARha2CAIAcIShen/jsmXLtH37dnV2duqOO+7QrFmzlJ+frzVr1ujjjz+WyWTShAkT\nNHfu3OEMHwAwCCRYAAD0YSje33g4CTtSWVnZIGYKADiZsEUQAAAAAMKEBAsAAAAAwoQECwAAAADC\nhAQLAAAAAMKEBAsAAAAAwoQECwAAAADChAQLAAAAAMKEBAsAAAAAwoQEC/+/vbuPi6pO/z/+Hu5U\nBJEZUFbFO5QUu3EVS0nLG6xWrZQUu7PNm2gX09TuvmqWu2brZobizeYmGrqmphuaj183m7Lesu5i\naj2SXEWzRxaFMIggYsDM7w8fzoKCgR5mmPH1/IsZzvmc6zrnMJfXnM85AgAAADAIDRYAAAAAGIQG\nCwAAAAAMQoMFAAAAAAahwQIAAAAAg9BgAQAAAIBBaLAAAAAAwCA0WAAAAABgEBosAAAAADAIDRYA\nAAAAGIQGCwAAAAAMQoMFAAAAAAahwQIAAAAAg9BgAQAAAIBBaLAAAAAAwCA0WAAAAABgEBosAAAA\nADAIDRYAAAAAGIQGCwAAAAAM4uOsDR06dEirVq2SzWbToEGDNHz48Cq/Lysr05IlS3TixAkFBgZq\nypQpatGihSQpLS1N6enp8vLy0tixY9W9e/erjrl06VJlZWXJ399fkjRx4kS1b99edrtdq1at0sGD\nB9WoUSMlJiaqY8eOztoFAAAAADycU65g2Ww2paSkaMaMGUpKStLevXt16tSpKsukp6eradOmWrx4\nsYYOHaq1a9dKkk6dOqWMjAy99dZbmjlzplJSUmSz2X5xzDFjxmj+/PmaP3++2rdvL0k6ePCgfvzx\nRyUnJyshIUErVqxwRvoAAAAAbhBOuYKVnZ2tsLAwtWzZUpIUExOjzMxMtWnTxrHM/v37NWrUKElS\n7969tXLlStntdmVmZiomJka+vr5q0aKFwsLClJ2dLUm/OObl9u/fr7vuuksmk0mRkZE6d+6cCgoK\nFBwcXF+pAwDQIFU89YCrQ6ibtAxXRwAAteKUBstqtcpisTheWywWHTt2rMZlvL295e/vr6KiIlmt\nVnXu3NmxnNlsltVqdYxT05jr1q3Tpk2bdPPNN+uxxx6Tr6+vrFarQkJCqqxjtVqvaLC2bdumbdu2\nSZLmzZtXZZ26+uma13QNHx+f68q3ISM390Ru7smTcwMA4Gqcdg+WMz366KNq3ry5ysvLtXz5cm3Z\nskUjR46s9fqxsbGKjY11vM7Ly6uPMBuk8vJyj803JCSE3NwQubmn682tVatWBkYDAIDzOKXBMpvN\nys/Pd7zOz8+X2WyudhmLxaKKigqVlJQoMDDwinWtVqtj3ZrGvHRFytfXVwMGDNDWrVsd26hc8KuL\nAwAAqX4ezrRs2TIdOHBAQUFBWrBggWOs4uJiJSUl6fTp0woNDdXUqVMVEBDgvGQBAIZxykMuIiIi\nlJOTo9zcXJWXlysjI0PR0dFVlunZs6d27NghSdq3b5+6desmk8mk6OhoZWRkqKysTLm5ucrJyVGn\nTp2uOmZBQYEkOe7hCg8PlyRFR0dr165dstvtOnr0qPz9/bn/CgBwhfp4OJMk9e/fXzNmzLhie5s3\nb7bJlwIAABxTSURBVNYtt9yi5ORk3XLLLdq8eXP9JwkAqBdOuYLl7e2tcePGae7cubLZbBowYIDC\nw8O1YcMGRUREKDo6WgMHDtSSJUs0adIkBQQEaMqUKZKk8PBw9enTR9OmTZOXl5fGjx8vL6+LfWF1\nY0pScnKyzp49K0lq166dEhISJEm//vWvdeDAAU2ePFl+fn5KTEx0RvoAADdTHw9nioyMVFRUlHJz\nc6/YXmZmpmbPni1JuvvuuzV79mw9/vjj9Z8oAMBwTrsHq0ePHurRo0eV90aPHu342c/PT9OmTat2\n3bi4OMXFxdVqTEl69dVXqx3HZDJpwoQJdQkbAHADqq+HM9WksLDQMaOiefPmKiwsrHY5HsLkmQ9O\n8dTcPDUvqW65efLfmifndl3bqfctAACAWjOZTDKZTNX+jocweWa+nvrAG0/NS/Ls3Dz5b+16c6vt\nA5iccg8WAADupC4PZ5JU64cz1SQoKMhx/3BBQYGaNWtmVCoAACejwQIA4DL18XCmq4mOjtbOnTsl\nSTt37lSvXr3qJS8AQP1jiiAAAJepr4czLVy4UFlZWSoqKtLvfvc7xcfHa+DAgRo+fLiSkpKUnp7u\neEw7AMA90WDhmlU89YCrQ6ibtAxXRwDAjdTHw5kuNWGXCwwM1CuvvHId0QIAGgqmCAIAAACAQWiw\nAAAAAMAgNFgAAAAAYBAaLAAAAAAwCA0WAAAAABiEBgsAAAAADEKDBQAAAAAGocECAAAAAIPQYAEA\nAACAQWiwAAAAAMAgNFgAAAAAYBAaLAAAAAAwCA0WAAAAABiEBgsAAAAADEKDBQAAAAAGocECAAAA\nAIPQYAEAAACAQWiwAAAAAMAgNFgAAAAAYBAaLAAAAAAwCA0WAAAAABiEBgsAAAAADEKDBQAAAAAG\nocECAAAAAIPQYAEAAACAQWiwAAAAAMAgNFgAAAAAYBAaLAAAAAAwCA0WAAAAABiEBgsAAAAADOLj\nrA0dOnRIq1atks1m06BBgzR8+PAqvy8rK9OSJUt04sQJBQYGasqUKWrRooUkKS0tTenp6fLy8tLY\nsWPVvXv3q46ZnJys48ePy8fHRxEREUpISJCPj48OHz6sN954wzHuHXfcoZEjRzprFwAAAADwcE65\ngmWz2ZSSkqIZM2YoKSlJe/fu1alTp6osk56erqZNm2rx4sUaOnSo1q5dK0k6deqUMjIy9NZbb2nm\nzJlKSUmRzWa76ph9+/bVwoUL9eabb+rnn39Wenq6Yztdu3bV/PnzNX/+fJorAAAAAIZyyhWs7Oxs\nhYWFqWXLlpKkmJgYZWZmqk2bNo5l9u/fr1GjRkmSevfurZUrV8putyszM1MxMTHy9fVVixYtFBYW\npuzsbEmqccwePXo4xu3UqZPy8/OdkSYAwIM4c+bF0qVLlZWVJX9/f0nSxIkT1b59e+clCwAwjFMa\nLKvVKovF4nhtsVh07NixGpfx9vaWv7+/ioqKZLVa1blzZ8dyZrNZVqvVMc7VxiwvL9fu3bv15JNP\nOt47evSoXnjhBQUHB2vMmDEKDw+/It5t27Zp27ZtkqR58+YpJCTkGjOXfrrmNV3Dx8en1vl6cm7u\nhtzcE7k1XJdmSbz88suyWCyaPn26oqOjq3wxWHnmxd69e7V27VpNnTq1ysyLgoICzZkzR4sWLZKk\nq445ZswY9e7d2yX5AgCM47R7sFxhxYoV6tq1q7p27SpJ6tChg5YtW6bGjRvrwIEDmj9/vpKTk69Y\nLzY2VrGxsY7XeXl5TovZ1crLyz02X0/OLSQkhNzcELnVrFWrVgZGU3fOnnkBAPAcTrkHy2w2V5mm\nl5+fL7PZXOMyFRUVKikpUWBg4BXrWq1Wmc3mXxxz48aNOnv2rJ544gnHe/7+/mrcuLEkqUePHqqo\nqNDZs2eNTRYA4Paqm3lxafZEdctcPvOi8rqXZl780pjr1q3T888/r3fffVdlZWX1lRoAoJ455QpW\nRESEcnJylJubK7PZrIyMDE2ePLnKMj179tSOHTsUGRmpffv2qVu3bjKZTIqOjlZycrKGDRumgoIC\n5eTkqFOnTrLb7TWOuX37dn3xxRd65ZVX5OX1vx7yzJkzCgoKkslkUnZ2tmw2mwIDA52xCwAAqNGj\njz6q5s2bq7y8XMuXL9eWLVuqfRATU9jdd9rp1Xhqbp6al8QtFZd4cm7XtZ1634IufrM3btw4zZ07\nVzabTQMGDFB4eLg2bNigiIgIRUdHa+DAgVqyZIkmTZqkgIAATZkyRZIUHh6uPn36aNq0afLy8tL4\n8eMdTVN1Y0rSO++8o9DQUM2cOVPS/x7Hvm/fPv3jH/+Qt7e3/Pz8NGXKFJlMJmfsAgCAG6nLzAuL\nxVKrmReXxqluzODgYEmSr6+vBgwYoK1bt1YbF1PYPTNfT50u7Kl5SZ6dmyf/rV1vbrWdvu60e7B6\n9OhR5el+kjR69GjHz35+fpo2bVq168bFxSkuLq5WY0rS+vXrqx3nvvvu03333VeXsAEANyBnz7wo\nKChQcHCw4x6u6h7ABABwDx79kAsAAK6Fs2deJCcnO+4JbteunRISElyTOADgutFgAQBQDWfOvHj1\n1VevM1oAQEPhlKcIAgAAAMCNgAYLAAAAAAxCgwUAAAAABqHBAgAAAACD0GABAAAAgEFosAAAAADA\nIDRYAAAAAGAQGiwAAAAAMAgNFgAAAAAYhAYLAAAAAAxCgwUAAAAABvFxdQAAAABGqnjqAVeHUDdp\nGa6OAICBuIIFAAAAAAahwQIAAAAAg9BgAQAAAIBBaLAAAAAAwCA0WAAAAABgEBosAAAAADAIDRYA\nAAAAGIQGCwAAAAAMQoMFAAAAAAahwQIAAAAAg9BgAQAAAIBBaLAAAAAAwCA0WAAAAABgEBosAAAA\nADAIDRYAAAAAGMTH1QEADVHFUw+4OoS6SctwdQQAAAAQDRYAAABcjC824UlosIAbDEUMANwTn9+A\ne+AeLAAAAAAwCA0WAAAAABiEBgsAAAAADOK0e7AOHTqkVatWyWazadCgQRo+fHiV35eVlWnJkiU6\nceKEAgMDNWXKFLVo0UKSlJaWpvT0dHl5eWns2LHq3r37VcfMzc3VwoULVVRUpI4dO2rSpEny8fG5\n6jYAuD/uT4CRGkLdAgC4H6d8ettsNqWkpOjll1+WxWLR9OnTFR0drTZt2jiWSU9PV9OmTbV48WLt\n3btXa9eu1dSpU3Xq1CllZGTorbfeUkFBgebMmaNFixZJUo1j/u1vf9PQoUN155136q9//avS09N1\nzz331LgNAGjoaB6dq6HULQCA+3HKFMHs7GyFhYWpZcuW8vHxUUxMjDIzM6sss3//fvXv31+S1Lt3\nb3311Vey2+3KzMxUTEyMfH191aJFC4WFhSk7O7vGMe12uw4fPqzevXtLkvr37+/YVk3bAACgsoZS\ntwAA7scpV7CsVqssFovjtcVi0bFjx2pcxtvbW/7+/ioqKpLValXnzp0dy5nNZlmtVsc4l49ZVFQk\nf39/eXt7X7F8Tdto1qxZlVi2bdumbdu2SZLmzZunVq1aXXvy/2//ta/rIrXOl9waFHITuTUw1/XZ\n6WINpW5djvp0g/+9eGpeErk1MOR2fXjIRTViY2M1b948zZs3z9WhON3//d//uTqEekNu7onc3JMn\n5+ZK1CfP5Km5eWpeErm5K2fl5pQGy2w2Kz8/3/E6Pz9fZrO5xmUqKipUUlKiwMDAK9a1Wq0ym801\njhkYGKiSkhJVVFRUWf5q2wAAoLKGUrcAAO7HKQ1WRESEcnJylJubq/LycmVkZCg6OrrKMj179tSO\nHTskSfv27VO3bt1kMpkUHR2tjIwMlZWVKTc3Vzk5OerUqVONY5pMJnXr1k379u2TJO3YscOxrZq2\nAQBAZQ2lbgEA3I/37NmzZ9f3Rry8vBQWFqbFixfrk08+Ub9+/dS7d29t2LBBpaWlatWqldq2bas9\ne/bovffe08mTJ5WQkKCAgAAFBQWpuLhYy5cv1549ezRu3Di1atWqxjElqWPHjlq9erW2bNmipk2b\n6uGHH5a3t3eN20BVHTt2dHUI9Ybc3BO5uSd3zq2h1C1U5c7n1C/x1Nw8NS+J3NyVM3Iz2XmMHgAA\nAAAYgodcAAAAAIBBaLAAAAAAwCBO+X+w0PDl5eVp6dKlOnPmjEwmk2JjYzVkyBBXh2WYiRMnqnHj\nxvLy8pK3t7dbP+J42bJlOnDggIKCgrRgwQJJUnFxsZKSknT69GmFhoZq6tSpbnd/YU3n4Pvvv6/t\n27c7/r+6Rx55RD169HBxtHVX3TnorsetLueg3W7XqlWrdPDgQTVq1EiJiYkePbcfxqI2uQ9PrU0S\n9cmdjl2DqU92wG63W61W+/Hjx+12u91eUlJinzx5sv27775zcVTGSUxMtBcWFro6DEMcPnzYfvz4\ncfu0adMc761Zs8aelpZmt9vt9rS0NPuaNWtcFd41q+kc3LBhg33Lli0uju76VXcOuutxq8s5+Pnn\nn9vnzp1rt9ls9v/+97/26dOnuyRmuCdqk/vw1Npkt1Of3OnYNZT6xBRBSJKCg4MdXXuTJk3UunVr\nWa1WF0eF6kRFRV3xLVJmZqbuvvtuSdLdd9+tzMxMV4R2XW7Ec9Bdj1tdzsH9+/frrrvukslkUmRk\npM6dO6eCggKnxwz3dCN+LrgrT61N0o15HrrrsWso9YkpgrhCbm6uvvnmG3Xq1MnVoRhq7ty5kqTB\ngwcrNjbWxdEYq7CwUMHBwZKk5s2bq7Cw0MURXZ/K5+CRI0f06aefateuXerYsaOeeOIJt5imUJ3L\nz0FPOm415WK1WhUSEuJYzmKxyGq1OpYFaova5H486TPuEuqT+3FFfaLBQhWlpaVasGCBnnzySfn7\n+7s6HMPMmTNHZrNZhYWFeu2119SqVStFRUW5Oqx6YTKZ3Po/0L78HLznnns0cuRISdKGDRu0evVq\nJSYmujjKuqvuHKzM3Y9bZZ6UCxoGapP784TPBeqT+3NWLkwRhEN5ebkWLFigfv366Y477nB1OIYy\nm82SpKCgIPXq1UvZ2dkujshYQUFBjsvaBQUFjhtu3U1152Dz5s3l5eUlLy8vDRo0SMePH3dxlNem\nunPQU46bVPM5aDablZeX51guPz/fsS+A2qA2uS9P+oyjPrnvsXNFfaLBgiTJbrfr7bffVuvWrTVs\n2DBXh2Oo0tJSnT9/3vHzl19+qbZt27o4KmNFR0dr586dkqSdO3eqV69eLo6o7mo6ByvPh/7Pf/6j\n8PBwV4R3XWo6Bz3huF1SUy7R0dHatWuX7Ha7jh49Kn9/f6YHotaoTe7NUz7jqE/ue+wk19Qnk91u\ntxsyEtzakSNH9Morr6ht27aOS6fu+rjRy/3000968803JUkVFRXq27ev4uLiXBzVtVu4cKGysrJU\nVFSkoKAgxcfHq1evXkpKSlJeXp5bPU61sprOwb179+rkyZMymUwKDQ1VQkKC2/0DvaZzsKioyC2P\nW13OQbvdrpSUFH3xxRfy8/NTYmKiIiIiXJ0C3AS1yX14am2SqE/udOwaSn2iwQIAAAAAgzBFEAAA\nAAAMQoMFAAAAAAahwQIAAAAAg9BgAQAAAIBBaLAAAAAAwCA0WPB4S5cu1fr1612ybbvdrmXLlmns\n2LGaPn16vWwjLy9PY8aMkc1m+8Vlc3NzFR8fr4qKinqJBQBQe9Sn/6E+wZPQYMHpJk6cqAkTJqi0\ntNTx3vbt2zV79mzXBVVPjhw5oi+//FJ/+ctf9Kc//alethESEqI1a9bIy+v6/5zff/99JScnGxBV\nw0PxBvBLqE/Goj7VDvXJ89BgwSVsNps++ugjV4dRZ7X5Fq6y06dPKzQ0VI0bN66niNyX3W6v8/50\nJQofcGOgPoH6hOvl4+oAcGN64IEHtGXLFt17771q2rRpld/l5ubqmWee0bp16+Tt7S1Jmj17tvr1\n66dBgwZpx44d2r59uyIiIrRjxw4FBARo0qRJysnJ0YYNG1RWVqbHH39c/fv3d4x59uxZzZkzR8eO\nHVOHDh30zDPPKDQ0VJL0/fffa+XKlTpx4oSaNWum0aNHKyYmRtLF6Rt+fn7Ky8tTVlaWXnjhBd16\n661V4rVarXrnnXd05MgRBQQE6MEHH1RsbKzS09OVkpKi8vJyjRkzRvfff7/i4+OrrJuYmKjnn39e\nHTt21O7du7V48WItWLBA4eHhSk9P1/79+/Xiiy/KZrPpww8/1Pbt23Xu3DndfPPNSkhIUEBAwBX7\nKzc3V0uXLtU333yjzp0761e/+pVKSko0efJkx3Z3796tDRs26Oeff9bQoUMVFxenQ4cOKS0tTZKU\nmZmpsLAwzZ8//4pjN3HiRMXGxmrXrl06c+aMevXqpQkTJsjPz0/FxcVasmSJjh07JpvNpptuuklP\nPfWULBaL4zjedNNNysrK0okTJ7RgwQJ9/fXX+vDDD5Wfn69mzZrpwQcf1ODBgyVJhw8f1uLFi/Wb\n3/xGW7dulZeXlyZMmCAfHx+lpqbq7Nmzuv/++xUXFydJV91Pr776qiTpySeflCTNmjVLkZGRSk9P\n19atW3XmzBl16tRJCQkJjnMjPj5e48aN00cffaSKigotWbJEqamp2rNnj8rKyhQSEqJnn31Wbdu2\nrc1pD8ANUJ8uoj5Rn3DtuIIFl+jYsaO6deumrVu3XtP6x44dU7t27bRy5Ur17dtXCxcuVHZ2tpKT\nkzVp0iStXLmyyhSPPXv26KGHHlJKSorat2/vmGZQWlqq1157TX379tWKFSs0ZcoUpaSk6NSpU1XW\nHTFihFJTU9WlS5crYlm0aJEsFouWL1+u5557TuvWrdNXX32lgQMH6qmnnlJkZKTWrFlzRfGSpKio\nKB0+fFiSlJWVpZYtW+rrr792vI6KipIkffLJJ8rMzNTs2bO1fPlyBQQEaMWKFdXum0WLFikiIkIr\nV67UqFGjtHv37iuWOXLkiBYtWqRZs2Zp06ZNOnXqlLp3764RI0aoT58+WrNmTbXFq/I+mTlzphYv\nXqycnBx98MEHki5+69e/f38tW7ZMy5Ytk5+fn1JSUqqsu2vXLiUkJGj16tUKCQlRUFCQXnrpJaWm\npioxMVGpqak6ceKEY/kzZ86orKxMb7/9tuLj47V8+XLt3r1b8+bN0x//+Ef9/e9/V25u7i/upz/8\n4Q+SpHfffVdr1qxRZGSkMjMzlZaWpueee04rVqxQly5dtGjRoirxZmZm6vXXX1dSUpK++OILff31\n11q0aJHeffddTZ06VYGBgTXuJwDuh/p0EfWJ+oRrR4MFl4mPj9fHH3+ss2fP1nndFi1aaMCAAfLy\n8lJMTIzy8/M1cuRI+fr66rbbbpOPj49+/PFHx/I9evRQVFSUfH199cgjj+jo0aPKy8vTgQMHFBoa\nqgEDBsjb21sdOnTQHXfcoX/961+OdXv16qUuXbrIy8tLfn5+VeLIy8vTkSNH9Nhjj8nPz0/t27fX\noEGDtHPnzlrlERUVpaysLEkXi8rw4cMdrysXsM8++0wPP/ywLBaLfH19NWrUKP373/++YlpAXl6e\njh8/rtGjR8vHx0ddunRRz549r9juqFGjHPG2a9dO3377ba3iveTee+9VSEiIAgICNGLECO3du1eS\nFBgYqN69e6tRo0Zq0qSJ4uLiHAX5kv79+ys8PFze3t7y8fFRjx49FBYWJpPJpKioKN166606cuSI\nY3lvb2/FxcXJx8dHd955p4qKijRkyBA1adJE4eHhatOmjU6ePFmn/XTJZ599phEjRqhNmzby9vbW\niBEjdPLkSZ0+fdqxzIgRIxQQECA/Pz/5+PiotLRU33//vex2u9q0aaPg4OA67TsADR/1ifpEfcL1\nYIogXKZt27bq2bOnNm/erNatW9dp3aCgIMfPl4pK8+bNq7xX+RvCS1MAJKlx48YKCAhQQUGBTp8+\nrWPHjjkuy0sX5zLfdddd1a57uYKCAgUEBKhJkyaO90JCQnT8+PFa5REVFaU1a9aooKBANptNffr0\n0aZNm5Sbm6uSkhK1b99e0sW58m+++aZMJpNjXS8vLxUWFlYZz2q1KiAgQI0aNaoST15eXpXlKu+r\nRo0aVdlXtRESEuL4OTQ0VFarVZJ04cIFpaam6tChQzp37pwk6fz587LZbI6bnC/fnwcPHtSmTZv0\nww8/yG6368KFC1WmNAQGBjrWvXSsLz/+l+Kv7X665PTp01q1apVWr17teM9ut8tqtTqmYVSO9+ab\nb9a9996rlJQU5eXl6fbbb9eYMWPk7+9fq/0GwD1Qn6hPEvUJ144GCy4VHx+vl156ScOGDXO8d+mG\n2wsXLjg+GM6cOXNd28nPz3f8XFpaquLiYgUHB8tisSgqKkqzZs2qcd3KH4aXCw4OVnFxsc6fP+8o\nYnl5eTKbzbWKKywsTH5+fvr444/VtWtX+fv7q3nz5tq2bZvjW0np4ofo73//+2qngFyaflA5ngsX\nLjiK2OXF62qulmtllcesnO/WrVv1ww8/6PXXX1fz5s118uRJvfjii7Lb7dVuo6ysTAsWLNAzzzyj\n6Oho+fj46I033qh1vJe72n6q/K3fJSEhIYqLi1O/fv1qHPPyfTJkyBANGTJEhYWFSkpK0ocffqiH\nH374mmMG0DBRn6hP1CdcK6YIwqXCwsLUp08fffzxx473mjVrJrPZrN27d8tmsyk9PV0//fTTdW3n\n4MGDOnLkiMrLy7V+/XpFRkYqJCREPXv2VE5Ojnbt2qXy8nKVl5crOzu7yhz3qwkJCdFNN92k9957\nTz///LO+/fZb/fOf/7zqB+LloqKi9OmnnzqmW1z+WpIGDx6s9evXOz6Ez549q8zMzCvGCg0NVURE\nhDZu3Kjy8nIdPXpUn3/+ea1jCQoK0unTp3/x6Umffvqp8vPzVVxcrA8++EB9+vSRdPEfB35+fvL3\n91dxcbE2btx41XHKy8tVVlamZs2aydvbWwcPHtSXX35Z63gvd7X91KxZM5lMpirn0uDBg7V582Z9\n9913kqSSkpIq028ul52drWPHjqm8vFyNGjWSr6+vIY8fBtDwUJ+oT9QnXCuuYMHlRo4cecWNrk8/\n/bRWrFihdevWaeDAgYqMjLyubdx5553auHGjjh49qo4dO2rSpEmSpCZNmujll19WamqqUlNTZbfb\n1a5dO/32t7+t9djPPvus3nnnHT399NMKCAjQqFGjrniS09VERUVp79696tq1q+P11q1bHa+li99K\nSdJrr72mgoICBQUFqU+fPurVq9cV402aNEnLli3TuHHj1KlTJ8XExNT6cbN9+vTR7t27NX78eLVo\n0UJ//vOfq12ub9++jliio6P10EMPOeJMTk7W+PHjZTabNWzYsGoL7SVNmjTR2LFjlZSUpLKyMvXs\n2VPR0dG1irU6V9tPjRo1UlxcnGbNmqWKigrNmDFDt99+u0pLS7Vw4ULl5eXJ399ft9xyi6MgX+78\n+fNKTU3VTz/9JD8/P91222164IEHrjleAA0b9Yn6RH3CtTDZK18bBeBxkpKS1Lp162qfEnUtJk6c\nqKeffrpORRoAgMtRn+CpuHYIeJjs7Gz9+OOPstlsOnTokPbv31/tN4kAADgT9Qk3CqYIAh7mzJkz\nWrBggYqKimSxWDRhwgR16NDB1WEBAG5w1CfcKJgiCAAAAAAGYYogAAAAABiEBgsAAAAADEKDBQAA\nAAAGocECAAAAAIPQYAEAAACAQf4/Fhgj/Lukya0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(12, 6))\n", + "ax1 = fig.add_subplot(1, 2, 1)\n", + "ax1.bar(0.1 + np.arange(len(num_weight_list)), train_errors)\n", + "ax1.set_xticks(0.5 + np.arange(len(num_weight_list)))\n", + "ax1.set_xticklabels(num_weight_list)\n", + "ax1.set_xlabel('Number of weight parameters')\n", + "ax1.set_ylabel('Error')\n", + "ax1.set_title('Training set')\n", + "ax2 = fig.add_subplot(1, 2, 2)\n", + "ax2.bar(0.1 + np.arange(len(num_weight_list)), valid_errors)\n", + "ax2.set_xticks(0.5 + np.arange(len(num_weight_list)))\n", + "ax2.set_xticklabels(num_weight_list)\n", + "ax2.set_xlabel('Number of weight parameters')\n", + "ax2.set_ylabel('Error')\n", + "ax2.set_title('Validation set')\n", + "fig.tight_layout()" + ] }, { "cell_type": "markdown", @@ -342,12 +421,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "scrolled": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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Iim0dJnG2eKn+LnGoLHNzYJeDAzgIMCqER2oJi9Rgi9Cg0w3OJY71GhWPZsTy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\nC0OGDCEgIKDGx05ISCjXVOpm8+bNY+/evQwfPpw5c+bUyXs5ffo0ycn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AZwVWn1zbL4ceGhoaCA8P774dHh5OUVFRn2XUajVGo5HW1lYaGhpITU3tLhcW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\n6lhfp3JK+SIhl1/PFxPoZmBeTAeGdvRogujF1SRREEII0eT0OoVx4d7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z515oZew4A2XH3Gx6p5XD+1y43SM3YVDSMkCngzPUagghOLzPxZH9HRyng9dd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\nhDB69GiOHj1Kjx49ePrpp/nnP/9Jjx49KCoqws3Njb/97W8cP36cefP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P5Ubk0IPVau324VBXV4fVau21jMfjwW63Y7FYjntufX39cc8dbDq1iofPT0aj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a8SGEoGp/gLqaIGlZhlOrTZBXiGdnOyHRypMX/IRwu8LXSl1cMz25z9LI0uiz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6XqJHwdSfNJhL+CHgVzBbks8hFbb+iT8YHH4foaBACFKGHkbCapNTGkcaJ09y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\nEMKm/wNpaqMOyNfePAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "inputs_grid = np.linspace(0., 1., 500)\n", + "true_func_grid = polynomial_function(inputs_grid, coefficients)\n", + "for num_weight, model in zip(num_weight_list, models):\n", + " fig = plt.figure(figsize=(8, 4))\n", + " ax = fig.add_subplot(111)\n", + " outputs_grid = model.fprop(inputs_grid)[-1]\n", + " ax.plot(inputs_train, targets_train, '.', label='training data')\n", + " ax.plot(inputs_grid, true_func_grid, label='true function')\n", + " ax.plot(inputs_grid, outputs_grid, label='fitted function')\n", + " ax.set_xlabel('Inputs $x$', fontsize=14)\n", + " ax.set_ylabel('Ouputs $y$', fontsize=14)\n", + " ax.set_title('{0} weight parameters'.format(num_weight))\n", + " ax.legend()" + ] }, { "cell_type": "markdown", @@ -355,6 +509,13 @@ "source": [ "You should be able to relate your answers to the questions above to what you see in these plots - ask a demonstrator if you are unsure what is going on. In particular for the models which appeared to be overfitting and generalising poorly you should now have an idea how this looks in terms of the model's predictions and how these relate to the training data points and true function values." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/05_Non-linearities_and_regularisation.ipynb b/notebooks/05_Non-linearities_and_regularisation.ipynb new file mode 100644 index 0000000..3f47450 --- /dev/null +++ b/notebooks/05_Non-linearities_and_regularisation.ipynb @@ -0,0 +1,541 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regularisation\n", + "\n", + "In this lab we will explore different methods for regularising networks to reduce overfitting and improve generalisation. This uses the material covered in the [fifth lecture slides](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp05-hid.pdf)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise 1: L1 and L2 penalties\n", + "\n", + "In the previous lab notebook we explored the issue of overfitting. There we saw that this arises when the model is 'too complex' ($\\sim$ has too many degrees of freedom / parameters) for the amount of data we have available.\n", + "\n", + "One method for trying to reduce overfitting is therefore to try to decrease the flexibility of the model. We can do this by simply reducing the number of free parameters in the model (e.g. by using a shallower model with fewer layers or layers with smaller dimensionality). More generally however we might want some way of more continuously varying the effective flexibility of a model with a fixed architecture.\n", + "\n", + "A common method for doing this is to add an additional term to the objective function being minimised during training which penalises some measure of the complexity of a model as a function of the model parameters. The aim of training is then to minimise with respect to the model parameters the sum $E^\\star$ of the data-driven error function term $\\bar{E}$ and a model complexity term $C$.\n", + "\n", + "\\begin{equation}\n", + " E^\\star =\n", + " \\underbrace{\\bar{E}}_{\\textrm{data term}} + \\underbrace{C}_{\\textrm{complexity term}}\n", + "\\end{equation}\n", + "\n", + "We need the complexity term $C$ to be easy to compute and differentiable with respect to the model parameters. A common choice is to use terms involving the *norms* ($\\sim$ a measure of size) of the parameters. This penalises models with large parameter values. Two commonly used norms are the L1 and L2 norms. \n", + "\n", + "For a $D$ dimensional vector $\\mathbf{v}$ the L1 norm is defined as\n", + "\n", + "\\begin{equation}\n", + "\\| \\boldsymbol{v} \\|_1 = \\sum_{d=1}^D \\left| v_d \\right|,\n", + "\\end{equation}\n", + "\n", + "and the L2 norm is defined as\n", + "\n", + "\\begin{equation}\n", + "\\| \\boldsymbol{v} \\|_2 = \\left[ \\sum_{d=1}^D \\left( v_d^2 \\right) \\right]^{\\frac{1}{2}}.\n", + "\\end{equation}\n", + "\n", + "For a $K \\times D$ matrix $\\mathbf{M}$, we will define norms by collapsing the matrix to a vector $\\boldsymbol{m} = \\mathrm{vec}\\left[\\mathbf{M}\\right] = \\left[ M_{1,1} \\dots M_{1,D} ~ M_{2,1} \\dots M_{K,D} \\right]^{\\rm T}$ and then taking the norm as defined above of this resulting vector (practically this just results in summing over two sets of indices rather than one).\n", + "\n", + "The overall complexity penalty term $C$ is defined as a sum over individual complexity terms for each of the $P$ parameters of the model \n", + "\n", + "\\begin{equation}\n", + " C = \\sum_{i=1}^P \\left[ C^{(i)} \\right]\n", + "\\end{equation}\n", + "\n", + "Some of these per-parameter penalty terms $C^{(i)}$ may be set to zero if we do not wish to penalise the size of the corresponding parameter.\n", + "\n", + "To enable us to tradeoff between the model complexity and data error terms, it is typical to introduce positive scalar coefficients $\\beta_i$ to scale the penalty term on the $i$th parameter. A *L1 penalty* on the $i$th vector parameter $\\boldsymbol{p}^{(i)}$ (or matrix parameter collapsed to a vector) is then commonly defined as\n", + "\n", + "\\begin{equation}\n", + " C^{(i)}_{\\textrm{L1}} = \n", + " \\beta_i \\left\\| \\boldsymbol{p}^{(i)} \\right\\|_1 = \n", + " \\beta_i \\sum_{d=1}^D \\left| p^{(i)}_d \\right|.\n", + "\\end{equation}\n", + "\n", + "This has a gradient with respect to the parameter vector\n", + "\n", + "\\begin{equation}\n", + " \\frac{\\partial C^{(i)}_{\\textrm{L1}}}{\\partial p^{(i)}_d} = \\beta_i \\, \\textrm{sgn}\\left( p^{(i)}_d \\right)\n", + "\\end{equation}\n", + "\n", + "where $\\textrm{sgn}(u) = +1$ if $u > 0$, $\\textrm{sgn}(u) = -1$ if $u < 0$ (and is not well defined for $u=0$ though a common convention is to have $\\textrm{sgn}(0) = 0$).\n", + "\n", + "Similarly a *L2 penalty* on the $i$th vector parameter $\\boldsymbol{p}^{(i)}$ (or matrix parameter collapsed to a vector) is commonly defined as\n", + "\n", + "\\begin{equation}\n", + " C^{(i)}_{\\textrm{L2}} = \n", + " \\frac{1}{2} \\beta_i \\left\\| \\boldsymbol{p}^{(i)} \\right\\|_2^2 =\n", + " \\frac{1}{2} \\beta_i \\sum_{d=1}^D \\left[ \\left( p^{(i)}_d \\right)^2 \\right].\n", + "\\end{equation}\n", + "\n", + "Somewhat confusingly this is proportional to the square of the L2 norm rather than the L2 norm itself, however it is an almost universal convention to call this an L2 penalty so we will stick with this nomenclature here. The $\\frac{1}{2}$ term is less universal and is sometimes not included; we include it here for consistency with how we defined the sum of squared errors cost. Similarly to that case, the $\\frac{1}{2}$ cancels when calculating the gradient with respect to the parameter\n", + "\n", + "\\begin{equation}\n", + " \\frac{\\partial C^{(i)}_{\\textrm{L2}}}{\\partial p^{(i)}_d} = \\beta_i p^{(i)}_d\n", + "\\end{equation}\n", + "\n", + "Use the above definitions for the L1 and L2 penalties for a parameter and corresponding gradients to implement the `__call__` and `grad` methods respectively for the skeleton `L1Penalty` and `L2Penalty` class definitions below. The `coefficient` propert of these classes should be used as the $\\beta_i$ value in the equations above. The parameter the penalty term (or gradient) is being evaluated for will be either a one or two-dimensional NumPy array (corresponding to a vector or matrix parameter respectively) and your implementations should be able to cope with both cases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "seed = 22102017 \n", + "rng = np.random.RandomState(seed)\n", + "class L1Penalty(object):\n", + " \"\"\"L1 parameter penalty.\n", + " \n", + " Term to add to the objective function penalising parameters\n", + " based on their L1 norm.\n", + " \"\"\"\n", + " \n", + " def __init__(self, coefficient):\n", + " \"\"\"Create a new L1 penalty object.\n", + " \n", + " Args:\n", + " coefficient: Positive constant to scale penalty term by.\n", + " \"\"\"\n", + " assert coefficient > 0., 'Penalty coefficient must be positive.'\n", + " self.coefficient = coefficient\n", + " \n", + " def __call__(self, parameter):\n", + " \"\"\"Calculate L1 penalty value for a parameter.\n", + " \n", + " Args:\n", + " parameter: Array corresponding to a model parameter.\n", + " \n", + " Returns:\n", + " Value of penalty term.\n", + " \"\"\"\n", + " raise NotImplementedError()\n", + " \n", + " def grad(self, parameter):\n", + " \"\"\"Calculate the penalty gradient with respect to the parameter.\n", + " \n", + " Args:\n", + " parameter: Array corresponding to a model parameter.\n", + " \n", + " Returns:\n", + " Value of penalty gradient with respect to parameter. This\n", + " should be an array of the same shape as the parameter.\n", + " \"\"\"\n", + " raise NotImplementedError()\n", + " \n", + " def __repr__(self):\n", + " return 'L1Penalty({0})'.format(self.coefficient)\n", + " \n", + "\n", + "class L2Penalty(object):\n", + " \"\"\"L1 parameter penalty.\n", + " \n", + " Term to add to the objective function penalising parameters\n", + " based on their L2 norm.\n", + " \"\"\"\n", + "\n", + " def __init__(self, coefficient):\n", + " \"\"\"Create a new L2 penalty object.\n", + " \n", + " Args:\n", + " coefficient: Positive constant to scale penalty term by.\n", + " \"\"\"\n", + " assert coefficient > 0., 'Penalty coefficient must be positive.'\n", + " self.coefficient = coefficient\n", + " \n", + " def __call__(self, parameter):\n", + " \"\"\"Calculate L2 penalty value for a parameter.\n", + " \n", + " Args:\n", + " parameter: Array corresponding to a model parameter.\n", + " \n", + " Returns:\n", + " Value of penalty term.\n", + " \"\"\"\n", + " raise NotImplementedError()\n", + " \n", + " def grad(self, parameter):\n", + " \"\"\"Calculate the penalty gradient with respect to the parameter.\n", + " \n", + " Args:\n", + " parameter: Array corresponding to a model parameter.\n", + " \n", + " Returns:\n", + " Value of penalty gradient with respect to parameter. This\n", + " should be an array of the same shape as the parameter.\n", + " \"\"\"\n", + " raise NotImplementedError()\n", + " \n", + " def __repr__(self):\n", + " return 'L2Penalty({0})'.format(self.coefficient)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test your implementations by running the cells below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_params_1 = np.array([[0.5, 0.3, -1.2, 5.8], [0.2, -3.1, 4.9, -5.0]])\n", + "test_params_2 = np.array([0.8, -0.6, -0.3, 1.5, 2.8])\n", + "true_l1_cost_1 = 10.5\n", + "true_l1_grad_1 = np.array([[0.5, 0.5, -0.5, 0.5], [0.5, -0.5, 0.5, -0.5]])\n", + "true_l1_cost_2 = 3.\n", + "true_l1_grad_2 = np.array([0.5, -0.5, -0.5, 0.5, 0.5])\n", + "l1 = L1Penalty(0.5)\n", + "if (not np.allclose(l1(test_params_1), true_l1_cost_1) or\n", + " not np.allclose(l1(test_params_2), true_l1_cost_2)):\n", + " print('L1Penalty.__call__ giving incorrect value(s).')\n", + "elif (not np.allclose(l1.grad(test_params_1), true_l1_grad_1) or \n", + " not np.allclose(l1.grad(test_params_2), true_l1_grad_2)):\n", + " print('L1Penalty.grad giving incorrect value(s).')\n", + "else:\n", + " print('All test values calculated correctly for L1Penalty')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_params_1 = np.array([[0.5, 0.3, -1.2, 5.8], [0.2, -3.1, 4.9, -5.0]])\n", + "test_params_2 = np.array([0.8, -0.6, -0.3, 1.5, 2.8])\n", + "true_l2_cost_1 = 23.52\n", + "true_l2_grad_1 = np.array([[0.25, 0.15, -0.6, 2.9], [0.1, -1.55, 2.45, -2.5]])\n", + "true_l2_cost_2 = 2.795\n", + "true_l2_grad_2 = np.array([0.4, -0.3, -0.15, 0.75, 1.4])\n", + "l2 = L2Penalty(0.5)\n", + "if (not np.allclose(l2(test_params_1), true_l2_cost_1) or\n", + " not np.allclose(l2(test_params_2), true_l2_cost_2)):\n", + " print('L2Penalty.__call__ giving incorrect value(s).')\n", + "elif (not np.allclose(l2.grad(test_params_1), true_l2_grad_1) or \n", + " not np.allclose(l2.grad(test_params_2), true_l2_grad_2)):\n", + " print('L2Penalty.grad giving incorrect value(s).')\n", + "else:\n", + " print('All test values calculated correctly for L2Penalty')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2: Training with regularisation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previously in the second laboratory you implemented a function `grads_wrt_params` to calculate the gradient of an error function with respect to the parameters of an affine model (layer), given gradients of the error function with respect to the model (layer) outputs.\n", + "\n", + "If we are training a model using a regularised objective function, we need to additionally calculate the gradients of the regularisation penalty terms with respect to the parameters and add these to the error function gradient terms. Following from the definition of the regularised objective $E^\\star$ above we have that the gradient of the overall objective with respect to the $d$th element of the $i$th model parameter is\n", + "\n", + "\\begin{equation}\n", + " \\frac{\\partial E^\\star}{\\partial p^{(i)}_d} =\n", + " \\frac{\\partial \\bar{E}}{\\partial p^{(i)}_d} + \n", + " \\frac{\\partial C}{\\partial p^{(i)}_d}\n", + "\\end{equation}\n", + "\n", + "We have already discussed in the second lab notebook how to calculate the error function gradient term $\\frac{\\partial \\bar{E}}{\\partial p^{(i)}_d}$. As the model complexity term is composed of a sum of per parameter terms and only one of these will depend on the $i$th parameter we can write\n", + "\n", + "\\begin{equation}\n", + " \\frac{\\partial C}{\\partial p^{(i)}_d} = \\frac{\\partial C^{(i)}}{\\partial p^{(i)}_d}\n", + "\\end{equation}\n", + "\n", + "which corresponds to the penalty term gradients you implemented above. To enable us to use the same `Optimiser` implementation that we have previously used to train models without regularisation, we have altered the implementation of the `AffineLayer` class (this being the only layer we currently have defined with parameters) to allow us to specify penalty terms on the weight matrix and bias vector when creating an instance of the class and to add the corresponding penalty gradients to the returned value from the `grads_wrt_params` method. \n", + "\n", + "The penalty terms need to be specified as a class matching the interface of the `L1Penalty` and `L2Penalty` classes you implemented above, defining both a `__call__` method to calculate the penalty value for a parameter and a `grad` method to calculate the gradient of the penalty with respect to the parameter. Separate penalties can be specified for the weight and bias parameters, with it common to only regularise the weight parameters. \n", + "\n", + "The penalty terms for a layer are specifed using the `weights_penalty` and `biases_penalty` arguments to the `__init__` method of the `AffineLayer` class. If either (or both) ofthese are set to `None` (the default) no regularisation is applied to the corresponding parameter.\n", + "\n", + "Using the `L1Penalty` and `L2Penalty` classes you implemented in the previous exercise, train models to classify MNIST digit images with\n", + "\n", + " * no regularisation\n", + " * an L1 penalty with coefficient $10^{-5}$ on the all of the weight matrix parameters\n", + " * an L1 penalty with coefficient $10^{-3}$ on the all of the weight matrix parameters\n", + " * an L2 penalty with coefficient $10^{-4}$ on the all of the weight matrix parameters\n", + " * an L2 penalty with coefficient $10^{-2}$ on the all of the weight matrix parameters\n", + " \n", + "The models should all have three affine layers interspersed with rectified linear layers (as implemented in the first exercise) and intermediate layers between the input and output should have dimensionalities of 100. The final output layer should be an `AffineLayer` (the model outputting the logarithms of the non-normalised class probabilties) and you should use the `CrossEntropySoftmaxError` as the error function (which calculates the softmax of the model outputs to convert to normalised class probabilities before calculating the corresponding multi-class cross entropy error). \n", + "\n", + "Use the `GlorotInit` class introduced in the first coursework to initialise the weights in all layers, using a gain of 0.5 (this adjusts for the fact that the rectified linear sets zeros all negative inputs), and initialises the biases to zero with a `ConstantInit` object. \n", + "\n", + "As an example the necessary parameter initialisers, model and error can be defined using\n", + "\n", + "```python\n", + "weights_init = GlorotUniformInit(0.5, rng)\n", + "biases_init = ConstantInit(0.)\n", + "input_dim, output_dim, hidden_dim = 784, 10, 100\n", + "model = MultipleLayerModel([\n", + " AffineLayer(input_dim, hidden_dim, weights_init, \n", + " biases_init, weights_penalty=weights_penalty),\n", + " ReluLayer(),\n", + " AffineLayer(hidden_dim, hidden_dim, weights_init, \n", + " biases_init, weights_penalty=weights_penalty),\n", + " ReluLayer(),\n", + " AffineLayer(hidden_dim, output_dim, weights_init, \n", + " biases_init, weights_penalty=weights_penalty)\n", + "])\n", + "error = CrossEntropySoftmaxError()\n", + "```\n", + "\n", + "This assumes all the relevant classes have been imported from their modules, a penalty object has been assigned to `weights_penalty` and a seeded random number generator assigned to `rng`.\n", + "\n", + "For each regularisation scheme, train the model for 100 epochs with a batch size of 50 and using a gradient descent with momentum learning rule with learning rate 0.01 and momentum coefficient 0.9. For each regularisation scheme you should store the run statistics (output of `Optimiser.train`) and the final values of the first layer weights for each of the trained models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the training set error against epoch number for all different regularisation schemes on the same axis. On a second axis plot the validation set error against epoch number for all the different regularisation schemes. Interpret and comment on what you see." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cell below defines two functions for visualising the first layer weights of the models trained above. The first plots a histogram of the weight values and the second plots the first layer weights as feature maps, i.e. each row of the first layer weight matrix (corresponding to the weights going from the input MNIST image to a particular first layer output) is visualised as a $28\\times 28$ image. In these feature maps white corresponds to negative weights, black to positive weights and grey to weights close to zero. \n", + "\n", + "Use these functions to plot a histogram and feature map visualisation for the first layer weights of each model trained above. You should try to interpret the plots in the context of what you were told in the lecture about the behaviour of L1 versus L2 regularisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_param_histogram(param, fig_size=(6, 3), interval=[-1.5, 1.5]):\n", + " \"\"\"Plots a normalised histogram of an array of parameter values.\"\"\"\n", + " fig = plt.figure(figsize=fig_size)\n", + " ax = fig.add_subplot(111)\n", + " ax.hist(param.flatten(), 50, interval, normed=True)\n", + " ax.set_xlabel('Parameter value')\n", + " ax.set_ylabel('Normalised frequency density')\n", + " return fig, ax\n", + "\n", + "def visualise_first_layer_weights(weights, fig_size=(5, 5)):\n", + " \"\"\"Plots a grid of first layer weights as feature maps.\"\"\"\n", + " fig = plt.figure(figsize=fig_size)\n", + " num_feature_maps = weights.shape[0]\n", + " grid_size = int(num_feature_maps**0.5)\n", + " max_abs = np.abs(model.params[0]).max()\n", + " tiled = -np.ones((30 * grid_size, \n", + " 30 * num_feature_maps // grid_size)) * max_abs\n", + " for i, fm in enumerate(model.params[0]):\n", + " r, c = i % grid_size, i // grid_size\n", + " tiled[1 + r * 30:(r + 1) * 30 - 1, \n", + " 1 + c * 30:(c + 1) * 30 - 1] = fm.reshape((28, 28))\n", + " ax = fig.add_subplot(111)\n", + " max_abs = np.abs(tiled).max()\n", + " ax.imshow(tiled, cmap='Greys', vmin=-max_abs, vmax=max_abs)\n", + " ax.axis('off')\n", + " fig.tight_layout()\n", + " plt.show()\n", + " return fig, ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 4: Random data augmentation\n", + "\n", + "Another technique mentioned in the lectures for trying to reduce overfitting is to artificially augment the training data set by performing random transformations to the original training data inputs. The idea is to produce further artificial inputs corresponding to the same target class as the original input. The closer the artificially generated inputs are to the appearing like the true inputs the better as they provide more realistic additional examples for the model to learn from.\n", + "\n", + "For the handwritten image inputs in the MNIST dataset, an obvious way to considering augmenting the dataset is to apply small rotations to the original images. Providing the rotations are small we would generally expect that what we would identify as the class of a digit image will remain the same.\n", + "\n", + "Implement a function which given a batch of MNIST images as 784-dimensional vectors, i.e. an array of shape `(batch_size, 784)`\n", + "\n", + " * chooses 25% of the images in the batch at random\n", + " * for each image in the 25% chosen, rotates the image by a random angle in $\\left[-30^\\circ,\\,30^\\circ\\right]$\n", + " * returns a new array of size `(batch_size, 784)` in which the rows corresponding to the 25% chosen images are the vectors corresponding to the new randomly rotated images, while the remaining rows correspond to the original images.\n", + " \n", + "You will need to make use of the [`scipy.ndimage.interpolation.rotate`](https://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.ndimage.interpolation.rotate.html#scipy.ndimage.interpolation.rotate) function which is imported below for you. For computational efficiency you should use bilinear interpolation by setting `order=1` as a keyword argument to this function rather than using the default of bicubic interpolation. Additionally you should make sure the original shape of the images is maintained by passing a `reshape=False` keyword argument." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.ndimage.interpolation import rotate\n", + "\n", + "def random_rotation(inputs, rng):\n", + " \"\"\"Randomly rotates a subset of images in a batch.\n", + " \n", + " Args:\n", + " inputs: Input image batch, an array of shape (batch_size, 784).\n", + " rng: A seeded random number generator.\n", + " \n", + " Returns:\n", + " An array of shape (batch_size, 784) corresponding to a copy\n", + " of the original `inputs` array with the randomly selected\n", + " images rotated by a random angle. The original `inputs`\n", + " array should not be modified.\n", + " \"\"\"\n", + " raise NotImplementedError()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the cell below to test your implementation. This uses the `show_batch_of_images` function we implemented in the first lab notebook to visualise the images in a batch before and after application of the random rotation transformation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlp.data_providers import MNISTDataProvider\n", + "import matplotlib.pyplot as plt\n", + "def show_batch_of_images(img_batch, fig_size=(3, 3)):\n", + " fig = plt.figure(figsize=fig_size)\n", + " batch_size, im_height, im_width = img_batch.shape\n", + " # calculate no. columns per grid row to give square grid\n", + " grid_size = int(batch_size**0.5)\n", + " # intialise empty array to tile image grid into\n", + " tiled = np.empty((im_height * grid_size, \n", + " im_width * batch_size // grid_size))\n", + " # iterate over images in batch + indexes within batch\n", + " for i, img in enumerate(img_batch):\n", + " # calculate grid row and column indices\n", + " r, c = i % grid_size, i // grid_size\n", + " tiled[r * im_height:(r + 1) * im_height, \n", + " c * im_height:(c + 1) * im_height] = img\n", + " ax = fig.add_subplot(111)\n", + " ax.imshow(tiled, cmap='Greys') #, vmin=0., vmax=1.)\n", + " ax.axis('off')\n", + " fig.tight_layout()\n", + " plt.show()\n", + " return fig, ax\n", + "\n", + "test_data = MNISTDataProvider('test', 100, rng=rng)\n", + "inputs, targets = test_data.next()\n", + "_ = show_batch_of_images(inputs.reshape((-1, 28, 28)))\n", + "transformed_inputs = random_rotation(inputs, rng)\n", + "_ = show_batch_of_images(transformed_inputs.reshape((-1, 28, 28)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 5: Training with data augmentation\n", + "\n", + "One simple way to use data augmentation is to just statically augment the training data set - for example we could iterate through the training dataset applying a transformation function like that implemented above to generate new artificial inputs, and use both the original and newly generated data in a new data provider object. We are quite limited however in how far we can augment the dataset by with a static method like this however - if we wanted to apply 9 random rotations to each image in the original datase, we would end up with a dataset with 10 times the memory requirements and that would take 10 times as long to run through each epoch.\n", + "\n", + "An alternative is to randomly augment the data on the fly as we iterate through the data provider in each epoch. In this method a new data provider class can be defined that inherits from the original data provider to be augmented, and provides a new `next` method which applies a random transformation function like that implemented in the previous exercise to each input batch before returning it. This method means that on every epoch a different set of training examples are provided to the model and so in some ways corresponds to an 'infinite' data set (although the amount of variability in the dataset will still be significantly less than the variability in all possible digit images). Compared to static augmentation, this dynamic augmentation scheme comes at the computational cost of having to apply the random transformation each time a new batch is provided. We can vary this overhead by changing the proportion of images in a batch randomly transformed.\n", + "\n", + "An implementation of this scheme has been provided for the MNIST data set in the `AugmentedMNISTDataProvider` object in the `mlp.data_providers` module. In addition to the arguments of the original `MNISTDataProvider.__init__` method, this additional takes a `transformer` argument, which should be a function which takes as arguments an inputs batch array and a random number generator object, and returns an array corresponding to a random transformation of the inputs. \n", + "\n", + "Train a model with the same architecture as in exercise 3 and with no L1 / L2 regularisation using a training data provider which randomly augments the training images using your `random_rotation` transformer function. Plot the training and validation set errors over the training epochs and compare this plot to your previous results from exercise 3. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlp.data_providers import AugmentedMNISTDataProvider\n", + "\n", + "aug_train_data = AugmentedMNISTDataProvider('train', rng=rng, transformer=random_rotation)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}