diff --git a/mlp/initialisers.py b/mlp/initialisers.py index 243adc2..8c8e252 100644 --- a/mlp/initialisers.py +++ b/mlp/initialisers.py @@ -63,3 +63,81 @@ class NormalInit(object): def __call__(self, shape): return self.rng.normal(loc=self.mean, scale=self.std, size=shape) + +class GlorotUniformInit(object): + """Glorot and Bengio (2010) random uniform weights initialiser. + + Initialises an two-dimensional parameter array using the 'normalized + initialisation' scheme suggested in [1] which attempts to maintain a + roughly constant variance in the activations and backpropagated gradients + of a multi-layer model consisting of interleaved affine and logistic + sigmoidal transformation layers. + + Weights are sampled from a zero-mean uniform distribution with standard + deviation `sqrt(2 / (input_dim * output_dim))` where `input_dim` and + `output_dim` are the input and output dimensions of the weight matrix + respectively. + + References: + [1]: Understanding the difficulty of training deep feedforward neural + networks, Glorot and Bengio (2010) + """ + + def __init__(self, gain=1., rng=None): + """Construct a normalised initilisation random initialiser object. + + Args: + gain: Multiplicative factor to scale initialised weights by. + Recommended values is 1 for affine layers followed by + logistic sigmoid layers (or another affine layer). + rng (RandomState): Seeded random number generator. + """ + self.gain = gain + if rng is None: + rng = np.random.RandomState(DEFAULT_SEED) + self.rng = rng + + def __call__(self, shape): + assert len(shape) == 2, ( + 'Initialiser should only be used for two dimensional arrays.') + std = self.gain * (2. / (shape[0] + shape[1]))**0.5 + half_width = 3.**0.5 * std + return self.rng.uniform(low=-half_width, high=half_width, size=shape) + + +class GlorotNormalInit(object): + """Glorot and Bengio (2010) random normal weights initialiser. + + Initialises an two-dimensional parameter array using the 'normalized + initialisation' scheme suggested in [1] which attempts to maintain a + roughly constant variance in the activations and backpropagated gradients + of a multi-layer model consisting of interleaved affine and logistic + sigmoidal transformation layers. + + Weights are sampled from a zero-mean normal distribution with standard + deviation `sqrt(2 / (input_dim * output_dim))` where `input_dim` and + `output_dim` are the input and output dimensions of the weight matrix + respectively. + + References: + [1]: Understanding the difficulty of training deep feedforward neural + networks, Glorot and Bengio (2010) + """ + + def __init__(self, gain=1., rng=None): + """Construct a normalised initilisation random initialiser object. + + Args: + gain: Multiplicative factor to scale initialised weights by. + Recommended values is 1 for affine layers followed by + logistic sigmoid layers (or another affine layer). + rng (RandomState): Seeded random number generator. + """ + self.gain = gain + if rng is None: + rng = np.random.RandomState(DEFAULT_SEED) + self.rng = rng + + def __call__(self, shape): + std = self.gain * (2. / (shape[0] + shape[1]))**0.5 + return self.rng.normal(loc=0., scale=std, size=shape) diff --git a/mlp/layers.py b/mlp/layers.py index cc4cdda..64a7d71 100644 --- a/mlp/layers.py +++ b/mlp/layers.py @@ -257,3 +257,63 @@ class SoftmaxLayer(Layer): def __repr__(self): return 'SoftmaxLayer' + +class RadialBasisFunctionLayer(Layer): + """Layer implementing projection to a grid of radial basis functions.""" + + def __init__(self, grid_dim, intervals=[[0., 1.]]): + """Creates a radial basis function layer object. + + Args: + grid_dim: Integer specifying how many basis function to use in + grid across input space per dimension (so total number of + basis functions will be grid_dim**input_dim) + intervals: List of intervals (two element lists or tuples) + specifying extents of axis-aligned region in input-space to + tile basis functions in grid across. For example for a 2D input + space spanning [0, 1] x [0, 1] use intervals=[[0, 1], [0, 1]]. + """ + num_basis = grid_dim**len(intervals) + self.centres = np.array(np.meshgrid(*[ + np.linspace(low, high, grid_dim) for (low, high) in intervals]) + ).reshape((len(intervals), -1)) + self.scales = np.array([ + [(high - low) * 1. / grid_dim] for (low, high) in intervals]) + + def fprop(self, inputs): + """Forward propagates activations through the layer transformation. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + + Returns: + outputs: Array of layer outputs of shape (batch_size, output_dim). + """ + return np.exp(-(inputs[..., None] - self.centres[None, ...])**2 / + self.scales**2).reshape((inputs.shape[0], -1)) + + def bprop(self, inputs, outputs, grads_wrt_outputs): + """Back propagates gradients through a layer. + + Given gradients with respect to the outputs of the layer calculates the + gradients with respect to the layer inputs. + + Args: + inputs: Array of layer inputs of shape (batch_size, input_dim). + outputs: Array of layer outputs calculated in forward pass of + shape (batch_size, output_dim). + grads_wrt_outputs: Array of gradients with respect to the layer + outputs of shape (batch_size, output_dim). + + Returns: + Array of gradients with respect to the layer inputs of shape + (batch_size, input_dim). + """ + num_basis = self.centres.shape[1] + return -2 * ( + ((inputs[..., None] - self.centres[None, ...]) / self.scales**2) * + grads_wrt_outputs.reshape((inputs.shape[0], -1, num_basis)) + ).sum(-1) + + def __repr__(self): + return 'RadialBasisFunctionLayer(grid_dim={0})'.format(self.grid_dim) diff --git a/mlp/schedulers.py b/mlp/schedulers.py new file mode 100644 index 0000000..4f53e7e --- /dev/null +++ b/mlp/schedulers.py @@ -0,0 +1,34 @@ +# -*- coding: utf-8 -*- +"""Training schedulers. + +This module contains classes implementing schedulers which control the +evolution of learning rule hyperparameters (such as learning rate) over a +training run. +""" + +import numpy as np + + +class ConstantLearningRateScheduler(object): + """Example of scheduler interface which sets a constant learning rate.""" + + def __init__(self, learning_rate): + """Construct a new constant learning rate scheduler object. + + Args: + learning_rate: Learning rate to use in learning rule. + """ + self.learning_rate = learning_rate + + def update_learning_rule(self, learning_rule, epoch_number): + """Update the hyperparameters of the learning rule. + + Run at the beginning of each epoch. + + Args: + learning_rule: Learning rule object being used in training run, + any scheduled hyperparameters to be altered should be + attributes of this object. + epoch_number: Integer index of training epoch about to be run. + """ + learning_rule.learning_rate = self.learning_rate diff --git a/notebooks/03_Multiple_layer_models.ipynb b/notebooks/03_Multiple_layer_models.ipynb index 613e9ef..6f681b2 100644 --- a/notebooks/03_Multiple_layer_models.ipynb +++ b/notebooks/03_Multiple_layer_models.ipynb @@ -4,9 +4,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Multiple layer models and Activation Functions\n", + "$\\newcommand{\\vct}[1]{\\boldsymbol{#1}}\n", + "\\newcommand{\\mtx}[1]{\\mathbf{#1}}\n", + "\\newcommand{\\tr}{^\\mathrm{T}}\n", + "\\newcommand{\\reals}{\\mathbb{R}}\n", + "\\newcommand{\\lpa}{\\left(}\n", + "\\newcommand{\\rpa}{\\right)}\n", + "\\newcommand{\\lsb}{\\left[}\n", + "\\newcommand{\\rsb}{\\right]}\n", + "\\newcommand{\\lbr}{\\left\\lbrace}\n", + "\\newcommand{\\rbr}{\\right\\rbrace}\n", + "\\newcommand{\\fset}[1]{\\lbr #1 \\rbr}\n", + "\\newcommand{\\pd}[2]{\\frac{\\partial #1}{\\partial #2}}$\n", "\n", - "In this notebook we will explore network models with multiple layers of transformations. This will build upon the single-layer affine model we looked at in the previous notebook and use material covered in the [second](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp02-sln.pdf) and [third](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp03-mlp.pdf) lectures.\n", + "# Multiple layer models\n", + "\n", + "In this notebook we will explore network models with multiple layers of transformations. This will build upon the single-layer affine model we looked at in the previous notebook and use material covered in the [second](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp02-sln.pdf) and [third](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp03-mlp.pdf) lectures.\n", "\n", "You will need to use these models for the experiments you will be running in the first coursework so part of the aim of this lab will be to get you familiar with how to construct multiple layer models in our framework and how to train them.\n", "\n", @@ -15,14 +28,14 @@ "Often when discussing (neural) network models, a network layer is taken to mean an input to output transformation of the form\n", "\n", "\\begin{equation}\n", - " \\boldsymbol{y} = \\boldsymbol{f}(\\mathbf{W} \\boldsymbol{x} + \\boldsymbol{b})\n", + " \\vct{y} = \\vct{f}(\\mtx{W} \\vct{x} + \\vct{b})\n", " \\qquad\n", " \\Leftrightarrow\n", " \\qquad\n", - " y_k = f\\left(\\sum_{d=1}^D \\left( W_{kd} x_d \\right) + b_k \\right)\n", + " y_k = f\\lpa\\sum_{d=1}^D \\lpa W_{kd} x_d \\rpa + b_k \\rpa\n", "\\end{equation}\n", "\n", - "where $\\mathbf{W}$ and $\\boldsymbol{b}$ parameterise an affine transformation as discussed in the previous notebook, and $f$ is a function applied elementwise to the result of the affine transformation. For example a common choice for $f$ is the logistic sigmoid function \n", + "where $\\mtx{W}$ and $\\vct{b}$ parameterise an affine transformation as discussed in the previous notebook, and $f$ is a function applied elementwise to the result of the affine transformation. For example a common choice for $f$ is the logistic sigmoid function \n", "\\begin{equation}\n", " f(u) = \\frac{1}{1 + \\exp(-u)}.\n", "\\end{equation}\n", @@ -48,7 +61,7 @@ "As you can see this `SigmoidLayer` class has a very lightweight definition, defining just two key methods:\n", "\n", " * `fprop` which takes a batch of activations at the input to the layer and forward propagates them to produce activates at the outputs (directly equivalently to the `fprop` method you implemented for then `AffineLayer` in the previous notebook),\n", - " * `brop` which takes a batch of gradients with respect to the outputs of the layer and backward propagates them to calculate gradients with respect to the inputs of the layer (explained in more detail below).\n", + " * `brop` which takes a batch of gradients with respect to the outputs of the layer and back-propagates them to calculate gradients with respect to the inputs of the layer (explained in more detail below).\n", " \n", "This `SigmoidLayer` class only implements the logistic sigmoid non-linearity transformation and so does not have any parameters. Therefore unlike `AffineLayer` it is derived directly from the base `Layer` class rather than `LayerWithParameters` and does not need to implement `grads_wrt_params` or `params` methods. \n", "\n", @@ -80,38 +93,34 @@ "\n", "\n", "\n", - "For a layer with parameters, the gradients with respect to the layer outputs are required to calculate gradients with respect to the layer parameters. Therefore by combining backward propagation of gradients through the model with computing the gradients with respect to parameters in the relevant layers we can calculate gradients of the error function with respect to all of the parameters of a multiple-layer model in a very efficient manner (in fact the computational cost of computing gradients with respect to all of the parameters of the model using this method will only be a constant factor times the cost of calculating the model outputs in the forwards pass).\n", + "For a layer with parameters, the gradients with respect to the layer outputs are required to calculate gradients with respect to the layer parameters. Therefore by combining back-propagation of gradients through the model with computing the gradients with respect to parameters in the relevant layers we can calculate gradients of the error function with respect to all of the parameters of a multiple-layer model in a very efficient manner (in fact the computational cost of computing gradients with respect to all of the parameters of the model using this method will only be a constant factor times the cost of calculating the model outputs in the forward pass).\n", "\n", "We so far have abstractly talked about calculating gradients with respect to the inputs of a layer using gradients with respect to the layer outputs. More concretely we will be using the chain rule for derivatives to do this, similarly to how we used the chain rule in exercise 4 of the previous notebook to calculate gradients with respect to the parameters of an affine layer given gradients with respect to the outputs of the layer.\n", "\n", - "In particular if our layer has a batch of $B$ vector inputs each of dimension $D$, $\\left\\lbrace \\boldsymbol{x}^{(b)} \\right\\rbrace_{b=1}^B$, and produces a batch of $B$ vector outputs each of dimension $K$, $\\left\\lbrace \\boldsymbol{y}^{(b)}\\right\\rbrace_{b=1}^B$, then we can calculate the gradient with respect to the $d^\\textrm{th}$ dimension of the $b^{\\textrm{th}}$ input given the gradients with respect to the $b^{\\textrm{th}}$ output using\n", + "In particular if our layer has a batch of $B$ vector inputs each of dimension $D$, $\\fset{\\vct{x}^{(b)}}_{b=1}^B$, and produces a batch of $B$ vector outputs each of dimension $K$, $\\fset{\\vct{y}^{(b)}}_{b=1}^B$, then we can calculate the gradient with respect to the $d^\\textrm{th}$ dimension of the $b^{\\textrm{th}}$ input given the gradients with respect to the $b^{\\textrm{th}}$ output using\n", "\n", "\\begin{equation}\n", - " \\frac{\\partial \\bar{E}}{\\partial x^{(b)}_d} = \n", - " \\sum_{k=1}^K \\left( \n", - " \\frac{\\partial \\bar{E}}{\\partial y^{(b)}_k} \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} \n", - " \\right).\n", + " \\pd{E}{x^{(b)}_d} = \\sum_{k=1}^K \\lpa \\pd{E}{y^{(b)}_k} \\pd{y^{(b)}_k}{x^{(b)}_d} \\rpa.\n", "\\end{equation}\n", "\n", - "Mathematically therefore the `bprop` method takes an array of gradients with respect to the outputs $\\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d}$ and applies a sum-product operation with the partial derivatives of each output with respect to each input $\\frac{\\partial \\bar{E}}{\\partial y^{(b)}_k}$ to produce gradients with respect to the inputs of the layer $\\frac{\\partial \\bar{E}}{\\partial x^{(b)}_d}$.\n", + "Mathematically therefore the `bprop` method takes an array of gradients with respect to the outputs $\\pd{E}{y^{(b)}_k}$ and applies a sum-product operation with the partial derivatives of each output with respect to each input $\\pd{y^{(b)}_k}{x^{(b)}_d}$ to produce gradients with respect to the inputs of the layer $\\pd{E}{x^{(b)}_d}$.\n", "\n", - "For the affine transformation used in the `AffineLayer` implemented last week, i.e a forwards propagation corresponding to \n", + "For the affine transformation used in the `AffineLayer` implemented last week, i.e a forward propagation corresponding to \n", "\n", "\\begin{equation}\n", - " y^{(b)}_k = \\sum_{d=1}^D \\left( W_{kd} x^{(b)}_d \\right) + b_k\n", + " y^{(b)}_k = \\sum_{d=1}^D \\lpa W_{kd} x^{(b)}_d \\rpa + b_k\n", "\\end{equation}\n", "\n", "then the corresponding partial derivatives of layer outputs with respect to inputs are\n", "\n", "\\begin{equation}\n", - " \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} = W_{kd}\n", + " \\pd{y^{(b)}_k}{x^{(b)}_d} = W_{kd}\n", "\\end{equation}\n", "\n", - "and so the backwards-propagation method for the `AffineLayer` takes the following form\n", + "and so the back-propagation method for the `AffineLayer` takes the following form\n", "\n", "\\begin{equation}\n", - " \\frac{\\partial \\bar{E}}{\\partial x^{(b)}_d} = \n", - " \\sum_{k=1}^K \\left( \\frac{\\partial \\bar{E}}{\\partial y^{(b)}_k} W_{kd} \\right).\n", + " \\pd{E}{x^{(b)}_d} = \\sum_{k=1}^K \\lpa \\pd{E}{y^{(b)}_k} W_{kd} \\rpa.\n", "\\end{equation}\n", "\n", "This can be efficiently implemented in NumPy using the `dot` function\n", @@ -125,11 +134,10 @@ " return grads_wrt_outputs.dot(self.weights)\n", "```\n", "\n", - "An important special case applies when the outputs of a layer are an elementwise function of the inputs such that $y^{(b)}_k$ only depends on $x^{(b)}_d$ when $d = k$. In this case the partial derivatives $\\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d}$ will be zero for $k \\neq d$ and so the above summation collapses to a single term, giving\n", + "An important special case applies when the outputs of a layer are an elementwise function of the inputs such that $y^{(b)}_k$ only depends on $x^{(b)}_d$ when $d = k$. In this case the partial derivatives $\\pd{y^{(b)}_k}{x^{(b)}_d}$ will be zero for $k \\neq d$ and so the above summation collapses to a single term, giving\n", "\n", "\\begin{equation}\n", - " \\frac{\\partial \\bar{E}}{\\partial x^{(b)}_d} = \n", - " \\frac{\\partial \\bar{E}}{\\partial y^{(b)}_d} \\frac{\\partial y^{(b)}_d}{\\partial x^{(b)}_d}\n", + " \\pd{E}{x^{(b)}_d} = \\pd{E}{y^{(b)}_d} \\pd{y^{(b)}_d}{x^{(b)}_d}\n", "\\end{equation}\n", "\n", "i.e. to calculate the gradient with respect to the $b^{\\textrm{th}}$ input vector we just perform an elementwise multiplication of the gradient with respect to the $b^{\\textrm{th}}$ output vector with the vector of derivatives of the outputs with respect to the inputs. This case applies to the `SigmoidLayer` and to all other layers applying an elementwise function to their inputs.\n", @@ -141,9 +149,9 @@ " \\qquad\n", " \\Rightarrow\n", " \\qquad\n", - " \\frac{\\partial y^{(b)}_d}{\\partial x^{(b)}_d} = \n", - " \\frac{\\exp(-x^{(b)}_d)}{\\left[ 1 + \\exp(-x^{(b)}_d) \\right]^2} =\n", - " y^{(b)}_d \\left[ 1 - y^{(b)}_d \\right]\n", + " \\pd{y^{(b)}_d}{x^{(b)}_d} = \n", + " \\frac{\\exp(-x^{(b)}_d)}{\\lsb 1 + \\exp(-x^{(b)}_d) \\rsb^2} =\n", + " y^{(b)}_d \\lsb 1 - y^{(b)}_d \\rsb\n", "\\end{equation}\n", "\n", "which you should now be able relate to the implementation of `SigmoidLayer.bprop` given earlier:\n", @@ -177,9 +185,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -219,10 +225,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "def train_model_and_plot_stats(\n", @@ -238,7 +242,7 @@ "\n", " # Run the optimiser for 5 epochs (full passes through the training set)\n", " # printing statistics every epoch.\n", - " stats, keys, _ = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n", + " stats, keys, run_time = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n", "\n", " # Plot the change in the validation and training set error over training.\n", " fig_1 = plt.figure(figsize=(8, 4))\n", @@ -258,7 +262,7 @@ " ax_2.legend(loc=0)\n", " ax_2.set_xlabel('Epoch number')\n", " \n", - " return stats, keys, fig_1, ax_1, fig_2, ax_2" + " return stats, keys, run_time, fig_1, ax_1, fig_2, ax_2" ] }, { @@ -271,23 +275,212 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, + "source": [ + "### Varying initialisation scale\n", + "\n", + "First try a few different parameter initialisation scales" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `init_scale = 0.01`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 5: 1.2s to complete\n", - " error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n", - "Epoch 10: 1.2s to complete\n", - " error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n", - "Epoch 15: 1.1s to complete\n", - " error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n", - "Epoch 20: 0.7s to complete\n", - " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n" + "Epoch 5: 6.4s to complete\n", + " error(train)=3.10e-01, acc(train)=9.14e-01, error(valid)=2.91e-01, acc(valid)=9.18e-01\n", + "Epoch 10: 5.4s to complete\n", + " error(train)=2.88e-01, acc(train)=9.20e-01, error(valid)=2.76e-01, acc(valid)=9.23e-01\n", + "Epoch 15: 3.5s to complete\n", + " error(train)=2.78e-01, acc(train)=9.23e-01, error(valid)=2.69e-01, acc(valid)=9.24e-01\n", + "Epoch 20: 4.2s to complete\n", + " error(train)=2.71e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 25: 4.7s to complete\n", + " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.65e-01, acc(valid)=9.26e-01\n", + "Epoch 30: 3.7s to complete\n", + " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n", + "Epoch 35: 3.8s to complete\n", + " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 40: 4.2s to complete\n", + " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n", + "Epoch 45: 4.3s to complete\n", + " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n", + "Epoch 50: 4.0s to complete\n", + " error(train)=2.54e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n", + "Epoch 55: 3.6s to complete\n", + " error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n", + "Epoch 60: 4.4s to complete\n", + " error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 65: 3.8s to complete\n", + " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 70: 4.3s to complete\n", + " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 75: 3.5s to complete\n", + " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.57e-01, acc(valid)=9.30e-01\n", + "Epoch 80: 4.0s to complete\n", + " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n", + "Epoch 85: 3.9s to complete\n", + " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 90: 3.6s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.57e-01, acc(valid)=9.29e-01\n", + "Epoch 95: 4.6s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n", + "Epoch 100: 3.8s to complete\n", + " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n" ] + }, + { + "data": { + "image/png": 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kKqyvp5xOtMlPoK67CWPZGxjzn8OoD2/3vBBCiLYnoW2RxBgHv76hLwMTo3mu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\nHAzLRg3LBhpmgjtyCKNkJ5TsxPhiF8b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V2YcrozwYVr+N+su7KGcTZAzHMP3ncMmwdvcPF0KIU5FAL8R54tUVxWUNvL3b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\nxWKxnJTmkUceAcDlcrFx40bCw8MBaG5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "init_scale = 0.01 # scale for random parameter initialisation\n", + "learning_rate = 0.1 # learning rate for gradient descent\n", + "num_epochs = 100 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "\n", + "# Reset random number generator and data provider states on each run\n", + "# to ensure reproducibility of results\n", + "rng.seed(seed)\n", + "train_data.reset()\n", + "valid_data.reset()\n", + "\n", + "# Alter data-provider batch size\n", + "train_data.batch_size = batch_size \n", + "valid_data.batch_size = batch_size\n", + "\n", + "# Create a parameter initialiser which will sample random uniform values\n", + "# from [-init_scale, init_scale]\n", + "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + "# Create affine + softmax model\n", + "model = MultipleLayerModel([\n", + " AffineLayer(input_dim, output_dim, param_init, param_init),\n", + " SoftmaxLayer()\n", + "])\n", + "\n", + "# Initialise a cross entropy error object\n", + "error = CrossEntropyError()\n", + "\n", + "# Use a basic gradient descent learning rule\n", + "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + "_ = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `init_scale = 0.1`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 5: 3.8s to complete\n", + " error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n", + "Epoch 10: 4.0s to complete\n", + " error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n", + "Epoch 15: 3.7s to complete\n", + " error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n", + "Epoch 20: 5.4s to complete\n", + " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 25: 4.7s to complete\n", + " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 30: 4.2s to complete\n", + " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n", + "Epoch 35: 4.0s to complete\n", + " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 40: 4.3s to complete\n", + " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 45: 4.5s to complete\n", + " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n", + "Epoch 50: 3.7s to complete\n", + " error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 55: 3.7s to complete\n", + " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 60: 4.6s to complete\n", + " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n", + "Epoch 65: 4.3s to complete\n", + " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 70: 4.9s to complete\n", + " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n", + "Epoch 75: 4.7s to complete\n", + " error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 80: 4.7s to complete\n", + " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n", + "Epoch 85: 4.2s to complete\n", + " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n", + "Epoch 90: 4.4s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 95: 4.1s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 100: 4.2s to complete\n", + " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n" + ] + }, + { + "data": { + "image/png": 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DO9LGzz45yOf7KkN2LGWzoR7+Eerme9DXrED731+i+8yfXlUIIUT7IUnbZO5I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\n9Ry31tEr1kmYrWvHxExd6v/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MHIqWfhqER4Cug1IHvutHPvb9rED3HPlYV0eWP1gPoJEA6rUg6gzB3u9aMHVa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\ntGj/LmsrhBCi75KgPwl7bS4KdtuZNDS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -328,6 +521,641 @@ " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `init_scale = 0.5`" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 5: 3.7s to complete\n", + " error(train)=3.38e-01, acc(train)=9.03e-01, error(valid)=3.17e-01, acc(valid)=9.11e-01\n", + "Epoch 10: 4.3s to complete\n", + " error(train)=3.06e-01, acc(train)=9.13e-01, error(valid)=2.94e-01, acc(valid)=9.17e-01\n", + "Epoch 15: 3.3s to complete\n", + " error(train)=2.92e-01, acc(train)=9.17e-01, error(valid)=2.83e-01, acc(valid)=9.20e-01\n", + "Epoch 20: 5.5s to complete\n", + " error(train)=2.82e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.22e-01\n", + "Epoch 25: 3.8s to complete\n", + " error(train)=2.77e-01, acc(train)=9.22e-01, error(valid)=2.75e-01, acc(valid)=9.22e-01\n", + "Epoch 30: 4.3s to complete\n", + " error(train)=2.71e-01, acc(train)=9.24e-01, error(valid)=2.71e-01, acc(valid)=9.25e-01\n", + "Epoch 35: 3.9s to complete\n", + " error(train)=2.67e-01, acc(train)=9.25e-01, error(valid)=2.69e-01, acc(valid)=9.26e-01\n", + "Epoch 40: 4.4s to complete\n", + " error(train)=2.65e-01, acc(train)=9.27e-01, error(valid)=2.68e-01, acc(valid)=9.26e-01\n", + "Epoch 45: 4.2s to complete\n", + " error(train)=2.61e-01, acc(train)=9.27e-01, error(valid)=2.66e-01, acc(valid)=9.27e-01\n", + "Epoch 50: 4.2s to complete\n", + " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.65e-01, acc(valid)=9.27e-01\n", + "Epoch 55: 4.2s to complete\n", + " error(train)=2.57e-01, acc(train)=9.29e-01, error(valid)=2.64e-01, acc(valid)=9.29e-01\n", + "Epoch 60: 3.6s to complete\n", + " error(train)=2.56e-01, acc(train)=9.28e-01, error(valid)=2.65e-01, acc(valid)=9.28e-01\n", + "Epoch 65: 4.6s to complete\n", + " error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n", + "Epoch 70: 3.7s to complete\n", + " error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.64e-01, acc(valid)=9.28e-01\n", + "Epoch 75: 5.0s to complete\n", + " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n", + "Epoch 80: 3.7s to complete\n", + " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n", + "Epoch 85: 3.8s to complete\n", + " error(train)=2.48e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.30e-01\n", + "Epoch 90: 4.1s to complete\n", + " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n", + "Epoch 95: 4.3s to complete\n", + " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n", + "Epoch 100: 3.7s to complete\n", + " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n" + ] + }, + { + "data": { + "image/png": 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RXVP8VHohhBD+pHQjTr+2b9/OnDlz8Hg8jBw5kttvv73e+4sXL2bFihWYzWYi\nIiJ46KGH6NixIwDvvvsuW7duRWtNnz59eOCBB1ANhM/Jkycv4yu1HcfPVjBz02l2ni4lzRnEQ9fG\nkuIIavJ+nA4HZz6aj/7336G4EHXDLajb/wsVHuGHUrcP0dHR5ObKpC2+JvXqe1Kn/uHreo2Li2vU\neuYXX3zxxUut4PF4+O1vf8uUKVO44447mDNnDj179iQi4twv/MrKSn7wgx8wevRoKioqWLFiBYMH\nD2bv3r18/vnn/O53v+OWW25h4cKFxMbG0qlTp0sWqqhIrsECRAZZGJ4UQedwG+uOFfHx3nwKK6rp\nER2Mzdz4KxuhoaGURXdG3XAzVLnRq5egVy8BexB0TUGZ5CpJU4WEhFBa2rx+B+LipF59T+rUP3xd\nr+Hh4Y1ar8Hf1gcOHCA2NpaYmBgsFgtDhgxh06ZN9dbp3bs3drsdgLS0NFwuY7AQpRSVlZW43W6q\nqqqorq4mMjKyqd+lXVNK8d2kSN4al8yotA58ujefRz4+xOojhU2+Rq1CwjD9YCKmF/4AV6Wi//lX\nPC8/IWOXCyFEK9FgaLtcLpxOp/e50+n0hvKFrFy5kv79+wPQrVs3evXqxU9/+lN++tOf0q9fP+Lj\n431Q7PYnzGZm0jWxTB+ViDPEymvrTvLCyuNkFTb9li4V1xXTky9hemgylJfhee15PDNeQeed8UPJ\nhRBC+IpPO6KtXr2aQ4cOUdvifurUKU6cOMGMGTMAePnll9m9ezdXX311ve2WL1/O8uXLAZg2bRrR\n0dG+LFabEh0N16R14aNdp5ix7gg/++QI934nnvuuiSfIar7gNhaL5cJ1evM49I03U/LhPyhZ+Hf0\nzs2Efv8+Qr/3Q1RNy4m4sIvWqbgsUq++J3XqH4Gq1wZD2+FwkJeX532el5eHw+E4b70dO3bwwQcf\n8OKLL2K1WgHYuHEjaWlpBAUZnacGDBjAvn37zgvtjIwMMjIyvM+l00TDboiz0mdsEnO25TB303E+\n++YUk66JIb1L2HnrNthhYsQ4TP0God+fQ8k/Z1Gy7ENMEybCgEENdhpsr6Rzj39Ivfqe1Kl/BKoj\nWoPN4ykpKWRnZ5OTk4Pb7SYzM5P09PR66xw+fJhZs2bx9NNP17tmHR0dze7du6mursbtdvPNN9/Q\npUuXJn4VcTEdgi08OSSO32QkYDMrXv4ii9+uyuJMSVWT96WcHTFNehrTL34DQcF4/vd3eN54AZ19\n3A8lF0L2ZtWHAAAgAElEQVQI0RyNuuVr69atzJ07F4/Hw/Dhw7nzzjuZP38+KSkppKen8/LLL3Ps\n2DE6dOgAGGH9zDPP4PF4+Nvf/sbu3bsB6N+/Pz/+8Y8bLJTc8tV0VdWaj/a4+NfOXBRwd59obrva\ngcWkmvwXoa6uRq/6DP3hPKgoRw0fixp3Nyok1H9foJWRsxf/kHr1PalT/wjUmXajQvtKk9Buvpzi\nKv625TQbsopJiLTx0DWx3Nira7MOLl1UiF70LnrNUgiLQN15H2rISLlFDPlF6C9Sr74ndeofEtp1\nSGhfvo1ZRczafJqcEjfdO4WSEG4hOSqIZIedpKgggiyND1599CCef86Eg3sgMQ3T3T9BpfTwY+lb\nPvlF6B9Sr74ndeofEtp1SGj7RoXbw0d7XOxxudl7upCiSg8ACugSYSM5Kogkh70mzIOIsF+49zmA\n1hq9YRX6/XfgrAs1eATq+z9GRUZdmS/TwsgvQv+QevU9qVP/CFRoy9jjbZjdYmJ872iio6M5c+YM\nuaVuDrnKOZRfzqH8Cr45U8rqo4Xe9aNDLCQ7gkiOOhfk0SEWlFLGY9B30f2vRX/yHvo/H6K3fYka\ndw9qxFiURQ4lIYTwN/lN204opegYaqVjqJXrEs4Nl1dY7uZQfgWH8ss57DJ+bsoqprb5Jdxurhfi\nyVF2Ot9xH6ahN+GZ/zf0e2+j1yzDdM9PUD0HBObLCSFEOyGh3c5FBFno39lC/87neoaXuz0cya/g\nYM1Z+eH8cj7em4/bY0S53ay4ulMIY+/8OQNv3A0L/obnjV/BgEGYxj+I6hgbqK8jhBBtmoS2OE+Q\nxUSPjsH06Bjsfa2qWpNVWFHTvF7B+uNF/GZVFl0inNz2o6nceGgVtk/n4/nVo6hb7kSN+r6MqiaE\nED4mHdHaAX90RHF7NJnHili028VBVzkRdjO3JtgYteNDIjcuB0dHTBMehIFD2uSoatK5xz+kXn1P\n6tQ/pCOaaFUsJsWwxAhuuCqcr3PK+HCPiwUHivl32C18964Mxm2ZT/yMV6BHX0x3/xTVpWugiyyE\nEK2ehLa4LEopeseE0DsmhBOFlXy0x8XKQ2f5z1X3MLDb97jtq4X0eelxTCNqR1U7f2x0IYQQjSOh\nLXymS4SNh66N5d6+0Xy2v4BP9uXzYo8fkdS9iHE7P+H6jY9gu+O/ZFQ1IYRoJglt4XMRQRZ+0Cea\nO3o6WHW4kA/32Pjj1XfzbnUJoz9fxc1rVhJx9wOopG6BLqoQQrQqEtrCb2xmEzeldiAjJZJt2SUs\n2u3iXfNo3q+uZMSC1YyL+YLOd45HRbTPUdWEEKKpJLSF3ymlGBgXxsC4MA7nl/PhrjMsMw9hiYZr\n537O91JCufrmkTKqmhBCNEB+S4orKikqiCduSOBHpVV8svU4S3UK6/PtdHtnDbdd7WRAei/CLjEG\nuhBCtGcS2iIgnCFW7huazF3XVrNy7U4+OhrM9CM2OLKfGHMVKbERpHYMI8URRIojiHAJciGEkNAW\ngRViMzN2RH9GVVSw6/N17N97lIOeUA6WxJN5wuFdLybM6g3wVAlyIUQ7JaEtWgSL3U7/USPod4uG\nowfQa/5D4aaNHLI5ONi5F4fsfTiYG0XmsSLvNp1CredC3GkE+aWmFxVCiNZOQlu0KEopSExDJaYR\nOf4B+m9ZR781y2Dph2C2UNz/eg73z+BgWBcO1kxq8uXxukFu8Z6R1wZ6RJAc5kKItkF+m4kWSwUF\no67PgOsz0CePodf8h7D1K+mzZRV9nJ1Q12egbhhJSaiDQ/nlHHCVc7Dm8eXxYu9+gi0mwmwmwuxm\nwmy1DxPhdjOhtcs2YzncbjwPtZkJsZowtcFx04UQrZdMGNIOtKUJA3RVFXr7BvTaZfDNdlAm6D0Q\n09CboO813tvGiiurOVQT4Hllboorqimu9FBcWV3z8FBcUU2V5+KHv0lBqPVc2IfazITbTITZzCRE\nR5IUpklzBmM1S7D7Sls6VlsKqVP/CNSEIRLa7UBb/U+rz5xCr1uOXrccClwQHmkMkTr0JlRsl0bt\no8LtqRfi9UK9spqiimpKapcrqymprKao0kNRRTVgzC3es1MIfWNC6BMbQnJUEGaThHhztdVjNZCk\nTv1DQrsOCW3fauv/aXV1NXy9Fc+a/8COjeDxQLfeqBtuQg0cgrL5fl5vW1gkq3ZnsfNUCTtOl3L8\nbCVgnJn3jgmhT0wIfWND6Rppa5NTk/pLWz9WA0Hq1D8ktOuQ0Pat9vSfVhe40F+uRK9ZBmdOQXAo\nqk86pPZAJfeA+ESU+fJ7mH+7TvPL3Ow8XcqOUyXsPF3KqeIqACLtZvrE1oR4TCidw60S4pfQno7V\nK0Xq1D9kPm0hfEB1cKBuvQt9y52w/2v02v+gd++AjavQADY7JHVDJXdHpfSA5B6o8IjL/tyoYAvD\nEiMYlmjsK6e4ip2njbPwHadKWXvU6OHuDLHQt+YsvE9MCB1DrZf92UKI9kNCW7RJymSC7n1Q3fug\ntQbXGfSB3XBoL/rgHvSyD4xmdYBOcaiU7pBytfEzrivKdHln453CrIwM68DIlA5orTlZVOU9C99y\nsoTPDxcC0DncSt8YI8B7x4QQZjNjUqAUKJCzciFEPRLaos1TSoGzE8rZCa67EQBdUQFH96MP7kUf\n2oPetRW+/Nw4Gw8KNs7GU2qa1JO7o0LDLuvzu0TY6BJh49ZuUXi05lhBhfcsfM3RQpYeKLjwthgB\nblKgUPWWveGuFCbOXzYpY51gqwlnsAVniBVniKXmYa15zUKI1SR/HAjRSkhoi3ZJ2e1GZ7VuvQGM\ns/Ezp9CH9sDBPcbZ+CfvobXH2KBzAiq5O6T0QKX0QDscl9j7pZmUIjEqiMSoIG7r4aDaoznoKmdP\nbhkVbg9ag6emTB4NWoMGPFqft+ypXQe8z433z21fUllNXpmb/XnlnK3p9V5XkEXhCLYSHWLBEWIh\nOsSKI9hSL+Aj7eYm9YrXWlNRrSmr8hgPt+f8ZXd1vdfK3ZoujiIc1mriwm3ERdiICjLLHxRC1CEd\n0doB6YjSPLq8DA7vQ9c0qXNoL5QY16ZVRAe4dhjq+pGo+KQAl7Txqqo9uMrc5Ja6ySt1k1daRV6Z\nG1ep8ZqrtApXmZvqb/1WMCvjun1tiIfbzFS46waw51sB7OESt8DXYzcrgq0m7BYT+WVuKut8eLDF\nRFyEjS7hRktFXE2LRedwKyFWGbK2MeT/v39I7/E6JLR9S/7T+obWGk6fQB/ci23vV1RsXAvVbuia\nYoT3tcNQYZffqS3QPFpztrya3NKqc2FeVhPwpW7vYDV2i4lgq4ng2p91ly/02gWWgyymemfwDqeT\n3UezOVlUxcnCSk4UVnCiqIqThRWcKXFT95dVVLDFuOwQbiMuwkqXcDtxETZiwqxYfHCvvNaaKo+m\n0q2pqPZQWa2pcHuoqDZaOTqHW4mwt/yWAPn/7x8S2nVIaPuW/Kf1vejoaM4cOYTesBqduRyOHQKL\nBfpdi+n6DOg5wCe3lrU3lzpWK9weThVXcaKwgpOFVZwoquBEYRUniyq9g92A0SoQE2ajS4SVLhF2\nwmwmb+BWVhsBXOHWVFYbAex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G/Px8IiJ86yzfe++9DBs2zL9HJ/qkCoebXaW+YfNdZU1UOz0ADLKYmJoUSXpc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\n9AItXp3HPi1mc1EjCyYMZH6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "init_scale = 0.5 # scale for random parameter initialisation\n", + "learning_rate = 0.1 # learning rate for gradient descent\n", + "num_epochs = 100 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "\n", + "# Reset random number generator and data provider states on each run\n", + "# to ensure reproducibility of results\n", + "rng.seed(seed)\n", + "train_data.reset()\n", + "valid_data.reset()\n", + "\n", + "# Alter data-provider batch size\n", + "train_data.batch_size = batch_size \n", + "valid_data.batch_size = batch_size\n", + "\n", + "# Create a parameter initialiser which will sample random uniform values\n", + "# from [-init_scale, init_scale]\n", + "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + "# Create affine + softmax model\n", + "model = MultipleLayerModel([\n", + " AffineLayer(input_dim, output_dim, param_init, param_init),\n", + " SoftmaxLayer()\n", + "])\n", + "\n", + "# Initialise a cross entropy error object\n", + "error = CrossEntropyError()\n", + "\n", + "# Use a basic gradient descent learning rule\n", + "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + "_ = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "|`init_scale`| Final `error(train)` | Final `error(valid)` |\n", + "|------------|----------------------|----------------------|\n", + "| 0.01 | 2.43e-01 | 2.58e-01 |\n", + "| 0.1 | 2.43e-01 | 2.59e-01 |\n", + "| 0.5 | 2.45e-01 | 2.62e-01 |\n", + "\n", + "\n", + "Larger initialisation scale of 0.5 seems to give slightly slower initial learning than smaller scales of 0.1 and 0.01 however difference is only slight suggesting for this shallow architecure training performance is not particularly sensitive to initialisation scale.\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Varying learning rate\n", + "\n", + "Now let's try some different values for learning rate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `learning_rate = 0.05`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 5: 4.0s to complete\n", + " error(train)=3.41e-01, acc(train)=9.05e-01, error(valid)=3.16e-01, acc(valid)=9.12e-01\n", + "Epoch 10: 4.0s to complete\n", + " error(train)=3.10e-01, acc(train)=9.14e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n", + "Epoch 15: 5.1s to complete\n", + " error(train)=2.97e-01, acc(train)=9.18e-01, error(valid)=2.82e-01, acc(valid)=9.21e-01\n", + "Epoch 20: 4.1s to complete\n", + " error(train)=2.88e-01, acc(train)=9.20e-01, error(valid)=2.76e-01, acc(valid)=9.23e-01\n", + "Epoch 25: 4.4s to complete\n", + " error(train)=2.83e-01, acc(train)=9.21e-01, error(valid)=2.73e-01, acc(valid)=9.24e-01\n", + "Epoch 30: 3.9s to complete\n", + " error(train)=2.77e-01, acc(train)=9.22e-01, error(valid)=2.69e-01, acc(valid)=9.24e-01\n", + "Epoch 35: 3.7s to complete\n", + " error(train)=2.74e-01, acc(train)=9.24e-01, error(valid)=2.67e-01, acc(valid)=9.25e-01\n", + "Epoch 40: 4.0s to complete\n", + " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 45: 3.7s to complete\n", + " error(train)=2.68e-01, acc(train)=9.26e-01, error(valid)=2.64e-01, acc(valid)=9.27e-01\n", + "Epoch 50: 4.7s to complete\n", + " error(train)=2.66e-01, acc(train)=9.26e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n", + "Epoch 55: 3.7s to complete\n", + " error(train)=2.64e-01, acc(train)=9.26e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n", + "Epoch 60: 4.8s to complete\n", + " error(train)=2.63e-01, acc(train)=9.26e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n", + "Epoch 65: 3.8s to complete\n", + " error(train)=2.61e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.27e-01\n", + "Epoch 70: 4.2s to complete\n", + " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 75: 4.3s to complete\n", + " error(train)=2.58e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n", + "Epoch 80: 4.5s to complete\n", + " error(train)=2.57e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n", + "Epoch 85: 4.2s to complete\n", + " error(train)=2.56e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 90: 4.5s to complete\n", + " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n", + "Epoch 95: 4.0s to complete\n", + " error(train)=2.54e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n", + "Epoch 100: 3.4s to complete\n", + " error(train)=2.53e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n" + ] + }, + { + "data": { + "image/png": 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pOxie4JVJCCHE6ZHQDpA0ZwiPXZpCUU0TT356gNomb9DKouwOtKtuQnvybzBo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\nJAwGj0ANHgGA0dQEe/Mwcr8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qxqBBfKQ3sKeODO8W2NbwoAs237lqa0UVfYz6JM+7WIjBANMvwnDpQpiaOaCu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\nmpiY2KtyPXaT09LSqKmpoa6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "init_scale = 0.1 # scale for random parameter initialisation\n", + "learning_rate = 0.05 # learning rate for gradient descent\n", + "num_epochs = 100 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "\n", + "# Reset random number generator and data provider states on each run\n", + "# to ensure reproducibility of results\n", + "rng.seed(seed)\n", + "train_data.reset()\n", + "valid_data.reset()\n", + "\n", + "# Alter data-provider batch size\n", + "train_data.batch_size = batch_size \n", + "valid_data.batch_size = batch_size\n", + "\n", + "# Create a parameter initialiser which will sample random uniform values\n", + "# from [-init_scale, init_scale]\n", + "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + "# Create affine + softmax model\n", + "model = MultipleLayerModel([\n", + " AffineLayer(input_dim, output_dim, param_init, param_init),\n", + " SoftmaxLayer()\n", + "])\n", + "\n", + "# Initialise a cross entropy error object\n", + "error = CrossEntropyError()\n", + "\n", + "# Use a basic gradient descent learning rule\n", + "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + "_ = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `learning_rate = 0.1`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 5: 5.1s to complete\n", + " error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n", + "Epoch 10: 3.4s to complete\n", + " error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n", + "Epoch 15: 3.8s to complete\n", + " error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n", + "Epoch 20: 3.4s to complete\n", + " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 25: 4.3s to complete\n", + " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 30: 3.9s to complete\n", + " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n", + "Epoch 35: 4.5s to complete\n", + " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 40: 5.9s to complete\n", + " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 45: 4.0s to complete\n", + " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n", + "Epoch 50: 4.1s to complete\n", + " error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 55: 5.4s to complete\n", + " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 60: 4.4s to complete\n", + " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n", + "Epoch 65: 3.4s to complete\n", + " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 70: 5.4s to complete\n", + " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n", + "Epoch 75: 3.7s to complete\n", + " error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 80: 4.4s to complete\n", + " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n", + "Epoch 85: 4.0s to complete\n", + " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n", + "Epoch 90: 5.1s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 95: 3.6s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 100: 3.9s to complete\n", + " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n" + ] + }, + { + "data": { + "image/png": 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DO9LGzz45yOf7KkN2LGWzoR7+Eerme9DXrED731+i+8yfXlUIIUT7IUnbZO5I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\n9Ry31tEr1kmYrWvHxExd6v/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MHIqWfhqER4Cug1IHvutHPvb9rED3HPlYV0eWP1gPoJEA6rUg6gzB3u9aMHVa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\ntGj/LmsrhBCi75KgPwl7bS4KdtuZNDS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "init_scale = 0.1 # scale for random parameter initialisation\n", + "learning_rate = 0.1 # learning rate for gradient descent\n", + "num_epochs = 100 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "\n", + "# Reset random number generator and data provider states on each run\n", + "# to ensure reproducibility of results\n", + "rng.seed(seed)\n", + "train_data.reset()\n", + "valid_data.reset()\n", + "\n", + "# Alter data-provider batch size\n", + "train_data.batch_size = batch_size \n", + "valid_data.batch_size = batch_size\n", + "\n", + "# Create a parameter initialiser which will sample random uniform values\n", + "# from [-init_scale, init_scale]\n", + "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + "# Create affine + softmax model\n", + "model = MultipleLayerModel([\n", + " AffineLayer(input_dim, output_dim, param_init, param_init),\n", + " SoftmaxLayer()\n", + "])\n", + "\n", + "# Initialise a cross entropy error object\n", + "error = CrossEntropyError()\n", + "\n", + "# Use a basic gradient descent learning rule\n", + "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + "_ = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `learning_rate = 0.2`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 5: 3.5s to complete\n", + " error(train)=2.90e-01, acc(train)=9.19e-01, error(valid)=2.77e-01, acc(valid)=9.22e-01\n", + "Epoch 10: 3.8s to complete\n", + " error(train)=2.75e-01, acc(train)=9.23e-01, error(valid)=2.69e-01, acc(valid)=9.25e-01\n", + "Epoch 15: 5.3s to complete\n", + " error(train)=2.66e-01, acc(train)=9.26e-01, error(valid)=2.64e-01, acc(valid)=9.26e-01\n", + "Epoch 20: 4.3s to complete\n", + " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 25: 4.9s to complete\n", + " error(train)=2.57e-01, acc(train)=9.28e-01, error(valid)=2.64e-01, acc(valid)=9.27e-01\n", + "Epoch 30: 5.0s to complete\n", + " error(train)=2.53e-01, acc(train)=9.29e-01, error(valid)=2.61e-01, acc(valid)=9.30e-01\n", + "Epoch 35: 4.2s to complete\n", + " error(train)=2.50e-01, acc(train)=9.30e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n", + "Epoch 40: 4.0s to complete\n", + " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 45: 4.4s to complete\n", + " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 50: 3.8s to complete\n", + " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.60e-01, acc(valid)=9.31e-01\n", + "Epoch 55: 3.9s to complete\n", + " error(train)=2.43e-01, acc(train)=9.32e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n", + "Epoch 60: 3.9s to complete\n", + " error(train)=2.44e-01, acc(train)=9.31e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n", + "Epoch 65: 3.8s to complete\n", + " error(train)=2.41e-01, acc(train)=9.34e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n", + "Epoch 70: 4.2s to complete\n", + " error(train)=2.40e-01, acc(train)=9.34e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n", + "Epoch 75: 3.7s to complete\n", + " error(train)=2.38e-01, acc(train)=9.34e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n", + "Epoch 80: 4.3s to complete\n", + " error(train)=2.38e-01, acc(train)=9.33e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n", + "Epoch 85: 3.2s to complete\n", + " error(train)=2.36e-01, acc(train)=9.35e-01, error(valid)=2.61e-01, acc(valid)=9.30e-01\n", + "Epoch 90: 4.1s to complete\n", + " error(train)=2.36e-01, acc(train)=9.34e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 95: 3.0s to complete\n", + " error(train)=2.37e-01, acc(train)=9.34e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n", + "Epoch 100: 3.5s to complete\n", + " error(train)=2.35e-01, acc(train)=9.35e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n" + ] + }, + { + "data": { + "image/png": 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P1er+aDNyXXy4M4c1B/MwNFzVJoLx3e10i639JLPMgjIWbM9i1f4zBJkVY7ra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\nnj24XC716/jx4+o+k8nEmjVrMBgMLFmyhKSkJGp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ZS9IliFIDIu0nxJaNcPoEOGhto9OvHguDRna6+eInS828lZ7P/qIaNArsLazm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\n9w8dOsT8+fOpr68nNjaWwsJCnE4ne/bsoaioiIa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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "init_scale = 0.1 # scale for random parameter initialisation\n", + "learning_rate = 0.2 # learning rate for gradient descent\n", + "num_epochs = 100 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "\n", + "# Reset random number generator and data provider states on each run\n", + "# to ensure reproducibility of results\n", + "rng.seed(seed)\n", + "train_data.reset()\n", + "valid_data.reset()\n", + "\n", + "# Alter data-provider batch size\n", + "train_data.batch_size = batch_size \n", + "valid_data.batch_size = batch_size\n", + "\n", + "# Create a parameter initialiser which will sample random uniform values\n", + "# from [-init_scale, init_scale]\n", + "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + "# Create affine + softmax model\n", + "model = MultipleLayerModel([\n", + " AffineLayer(input_dim, output_dim, param_init, param_init),\n", + " SoftmaxLayer()\n", + "])\n", + "\n", + "# Initialise a cross entropy error object\n", + "error = CrossEntropyError()\n", + "\n", + "# Use a basic gradient descent learning rule\n", + "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + "_ = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `learning_rate = 0.5`" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 5: 3.5s to complete\n", + " error(train)=2.79e-01, acc(train)=9.20e-01, error(valid)=2.74e-01, acc(valid)=9.23e-01\n", + "Epoch 10: 3.7s to complete\n", + " error(train)=2.68e-01, acc(train)=9.24e-01, error(valid)=2.72e-01, acc(valid)=9.26e-01\n", + "Epoch 15: 3.9s to complete\n", + " error(train)=2.55e-01, acc(train)=9.28e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 20: 3.5s to complete\n", + " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n", + "Epoch 25: 4.3s to complete\n", + " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.73e-01, acc(valid)=9.25e-01\n", + "Epoch 30: 3.5s to complete\n", + " error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.70e-01, acc(valid)=9.27e-01\n", + "Epoch 35: 3.2s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.69e-01, acc(valid)=9.27e-01\n", + "Epoch 40: 4.1s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.72e-01, acc(valid)=9.26e-01\n", + "Epoch 45: 4.3s to complete\n", + " error(train)=2.36e-01, acc(train)=9.35e-01, error(valid)=2.66e-01, acc(valid)=9.29e-01\n", + "Epoch 50: 3.7s to complete\n", + " error(train)=2.38e-01, acc(train)=9.33e-01, error(valid)=2.69e-01, acc(valid)=9.28e-01\n", + "Epoch 55: 3.9s to complete\n", + " error(train)=2.36e-01, acc(train)=9.34e-01, error(valid)=2.68e-01, acc(valid)=9.26e-01\n", + "Epoch 60: 4.0s to complete\n", + " error(train)=2.46e-01, acc(train)=9.29e-01, error(valid)=2.81e-01, acc(valid)=9.22e-01\n", + "Epoch 65: 4.1s to complete\n", + " error(train)=2.33e-01, acc(train)=9.35e-01, error(valid)=2.70e-01, acc(valid)=9.28e-01\n", + "Epoch 70: 3.6s to complete\n", + " error(train)=2.35e-01, acc(train)=9.36e-01, error(valid)=2.75e-01, acc(valid)=9.27e-01\n", + "Epoch 75: 4.4s to complete\n", + " error(train)=2.31e-01, acc(train)=9.36e-01, error(valid)=2.70e-01, acc(valid)=9.26e-01\n", + "Epoch 80: 3.6s to complete\n", + " error(train)=2.35e-01, acc(train)=9.34e-01, error(valid)=2.76e-01, acc(valid)=9.25e-01\n", + "Epoch 85: 4.0s to complete\n", + " error(train)=2.32e-01, acc(train)=9.35e-01, error(valid)=2.75e-01, acc(valid)=9.26e-01\n", + "Epoch 90: 3.6s to complete\n", + " error(train)=2.29e-01, acc(train)=9.37e-01, error(valid)=2.74e-01, acc(valid)=9.26e-01\n", + "Epoch 95: 4.2s to complete\n", + " error(train)=2.31e-01, acc(train)=9.35e-01, error(valid)=2.76e-01, acc(valid)=9.27e-01\n", + "Epoch 100: 3.4s to complete\n", + " error(train)=2.31e-01, acc(train)=9.36e-01, error(valid)=2.77e-01, acc(valid)=9.27e-01\n" + ] + }, + { + "data": { + "image/png": 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pecrtQv0sPDohiQeWHuTRlVk8PSm5xbNDuYtjLuYCEkJ92+weYEywDzed0/hy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\ndCjh4eEsWLCA3NxceYzmBhw6dIg1a9YwdepUzpw5w8KFCyk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XHP0DMeIWhGvtL5ESQqDd+xg0aWbZkCXhBPp7s5CfL4au16A99wbW3kJV9OwL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\nb77JD37wA4++IE9Z83kVNQ0t/OTWyE5VFNI+y4GmRlmOJsR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "init_scale = 0.1 # scale for random parameter initialisation\n", + "learning_rate = 0.5 # learning rate for gradient descent\n", + "num_epochs = 100 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "\n", + "# Reset random number generator and data provider states on each run\n", + "# to ensure reproducibility of results\n", + "rng.seed(seed)\n", + "train_data.reset()\n", + "valid_data.reset()\n", + "\n", + "# Alter data-provider batch size\n", + "train_data.batch_size = batch_size \n", + "valid_data.batch_size = batch_size\n", + "\n", + "# Create a parameter initialiser which will sample random uniform values\n", + "# from [-init_scale, init_scale]\n", + "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + "# Create affine + softmax model\n", + "model = MultipleLayerModel([\n", + " AffineLayer(input_dim, output_dim, param_init, param_init),\n", + " SoftmaxLayer()\n", + "])\n", + "\n", + "# Initialise a cross entropy error object\n", + "error = CrossEntropyError()\n", + "\n", + "# Use a basic gradient descent learning rule\n", + "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + "_ = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "|`learning_rate`| Final `error(train)` | Final `error(valid)` |\n", + "|---------------|----------------------|----------------------|\n", + "| 0.05 | $2.53\\times 10^{-1}$ | $2.59\\times 10^{-1}$|\n", + "| 0.1 | $2.43\\times 10^{-1}$ | $2.59\\times 10^{-1}$|\n", + "| 0.2 | $2.35\\times 10^{-1}$ | $2.63\\times 10^{-1}$|\n", + "| 0.5 | $2.31\\times 10^{-1}$ | $2.77\\times 10^{-1}$|\n", + "\n", + "\n", + "Increasing the learning rate, as would be expected, increase the speed of learning, with the final training error reached monotonically decreasing over the learning rates tested as the learning rate was increased. Note however the validation set error increases for larger learning rates - this suggests the model is overfitting to the data, with the larger learning rates causing the model to begin overfitting sooner - we could have afforded to halt learning earlier in these cases when there was no further improvement in the validation set error. Notice also the error curves for the largest learning rate value are much more noisy suggesting learning is becoming quite unstable with this large a step size, with a lot of the gradient descent steps overshooting and causing the error function value to increase.\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -344,15 +1172,15 @@ "lead to particularly simple forms for the gradients of the error function with respect to the inputs to the final layer. In particular for the latter softmax and cross entropy error function case we have that\n", "\n", "\\begin{equation}\n", - " y^{(b)}_k = \\textrm{Softmax}_k\\left(\\boldsymbol{x}^{(b)}\\right) = \\frac{\\exp(x^{(b)}_k)}{\\sum_{d=1}^D \\left\\lbrace \\exp(x^{(b)}_d) \\right\\rbrace}\n", + " y^{(b)}_k = \\textrm{Softmax}_k\\lpa\\vct{x}^{(b)}\\rpa = \\frac{\\exp(x^{(b)}_k)}{\\sum_{d=1}^D \\lbr \\exp(x^{(b)}_d) \\rbr}\n", " \\qquad\n", - " E^{(b)} = \\textrm{CrossEntropy}\\left(\\boldsymbol{y}^{(b)},\\,\\boldsymbol{t}^{(b)}\\right) = -\\sum_{d=1}^D \\left\\lbrace t^{(b)}_d \\log(y^{(b)}_d) \\right\\rbrace\n", + " E^{(b)} = \\textrm{CrossEntropy}\\lpa\\vct{y}^{(b)},\\,\\vct{t}^{(b)}\\rpa = -\\sum_{d=1}^D \\lbr t^{(b)}_d \\log(y^{(b)}_d) \\rbr\n", "\\end{equation}\n", "\n", "and it can be shown (this is an instructive mathematical exercise if you want a challenge!) that\n", "\n", "\\begin{equation}\n", - " \\frac{\\partial E^{(b)}}{\\partial x^{(b)}_d} = y^{(b)}_d - t^{(b)}_d.\n", + " \\pd{E^{(b)}}{x^{(b)}_d} = y^{(b)}_d - t^{(b)}_d.\n", "\\end{equation}\n", "\n", "The combination of `CrossEntropyError` and `SoftmaxLayer` used to train the model above calculate this gradient less directly by first calculating the gradient of the error with respect to the model outputs in `CrossEntropyError.grad` and then back-propagating this gradient to the inputs of the softmax layer using `SoftmaxLayer.bprop`.\n", @@ -360,13 +1188,13 @@ "Rather than computing the gradient in two steps like this we can instead wrap the softmax transformation in to the definition of the error function and make use of the simpler gradient expression above. More explicitly we define an error function as follows\n", "\n", "\\begin{equation}\n", - " E^{(b)} = \\textrm{CrossEntropySoftmax}\\left(\\boldsymbol{y}^{(b)},\\,\\boldsymbol{t}^{(b)}\\right) = -\\sum_{d=1}^D \\left\\lbrace t^{(b)}_d \\log\\left[\\textrm{Softmax}_d\\left( \\boldsymbol{y}^{(b)}\\right)\\right]\\right\\rbrace\n", + " E^{(b)} = \\textrm{CrossEntropySoftmax}\\lpa\\vct{y}^{(b)},\\,\\vct{t}^{(b)}\\rpa = -\\sum_{d=1}^D \\lbr t^{(b)}_d \\log\\lsb\\textrm{Softmax}_d\\lpa \\vct{y}^{(b)}\\rpa\\rsb\\rbr\n", "\\end{equation}\n", "\n", "with corresponding gradient\n", "\n", "\\begin{equation}\n", - " \\frac{\\partial E^{(b)}}{\\partial y^{(b)}_d} = \\textrm{Softmax}_d\\left( \\boldsymbol{y}^{(b)}\\right) - t^{(b)}_d.\n", + " \\pd{E^{(b)}}{y^{(b)}_d} = \\textrm{Softmax}_d\\lpa \\vct{y}^{(b)}\\rpa - t^{(b)}_d.\n", "\\end{equation}\n", "\n", "The final layer of the model will then be an affine transformation which produces unbounded output values corresponding to the logarithms of the unnormalised predicted class probabilities. An implementation of this error function is provided in `CrossEntropySoftmaxError`. The cell below sets up a model with a single affine transformation layer and trains it on MNIST using this new cost. If you run it with equivalent hyperparameters to one of your runs with the alternative formulation above you should get identical error and classification curves (other than floating point error) but with a minor improvement in training speed.\n" @@ -374,11 +1202,76 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 5: 0.4s to complete\n", + " error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n", + "Epoch 10: 0.4s to complete\n", + " error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n", + "Epoch 15: 0.4s to complete\n", + " error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n", + "Epoch 20: 0.4s to complete\n", + " error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 25: 0.4s to complete\n", + " error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n", + "Epoch 30: 0.4s to complete\n", + " error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n", + "Epoch 35: 0.4s to complete\n", + " error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 40: 0.4s to complete\n", + " error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n", + "Epoch 45: 0.4s to complete\n", + " error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n", + "Epoch 50: 0.4s to complete\n", + " error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 55: 0.4s to complete\n", + " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n", + "Epoch 60: 0.4s to complete\n", + " error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n", + "Epoch 65: 0.4s to complete\n", + " error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 70: 0.4s to complete\n", + " error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n", + "Epoch 75: 0.4s to complete\n", + " error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 80: 0.4s to complete\n", + " error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n", + "Epoch 85: 0.4s to complete\n", + " error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n", + "Epoch 90: 0.4s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 95: 0.4s to complete\n", + " error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n", + "Epoch 100: 0.4s to complete\n", + " error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n" + ] + }, + { + "data": { + "image/png": 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DO9LGzz45yOf7KkN2LGWzoR7+Eerme9DXrED731+i+8yfXlUIIUT7IUnbZO5I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\n9Ry31tEr1kmYrWvHxExd6v/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MHIqWfhqER4Cug1IHvutHPvb9rED3HPlYV0eWP1gPoJEA6rUg6gzB3u9aMHVa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\ntGj/LmsrhBCi75KgPwl7bS4KdtuZNDS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rw+12U1JSgsPh6NDG5XLh8XgAeOONN0hLS+t0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\nRK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Set training run hyperparameters\n", "batch_size = 100 # number of data points in a batch\n", @@ -416,6 +1309,19 @@ " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "This gives exactly the same training curves (and error / accuracy values over training) as the two runs with equivalent parameters above (second `init_scale` experiment and second `learning_rate` experiment).\n", + "\n", + "\n", + "\n", + "The times per epoch seems to be slightly lower on average (0.20s compared to 0.22s) suggesting the reformulation gives a small efficiency gain (though this will become less apparent in deeper architectures as the benefit only applies to the final layer).\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -448,6 +1354,16 @@ "You may wish to start with shorter pilot training runs (by decreasing the number of training epochs) for each of the model architectures to get an initial idea of appropriate hyperparameter settings before doing one or two longer training runs to assess the final performance of the architectures." ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# disable logging by setting handler to dummy object\n", + "logger.handlers = [logging.NullHandler()]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -457,12 +1373,245 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.10\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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DXqGPt45eYt0nlxiRFI3H7WDq4ARiI+S55yoiAnXnLPQd2XDgvcAzzv/zefS6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\nSbx/+SMc/wSCQ3wLlMSMGi0XogkhhBB9ZQ4xMnN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BSLxq2+hL59FZ69DZG6G2Bnr1Qz01HzU6ERUgzxKIzkuauRBuVtvgICO/TOJV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\nYOzYsZw8edLNVXmmvXv30rdvX8LDw91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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 1.75e-01\n", + " final error(valid) = 1.72e-01\n", + " final acc(train) = 9.50e-01\n", + " final acc(valid) = 9.53e-01\n", + " run time per epoch = 9.98\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.20\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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I0W5FinbX01rjLq+nwO1hx7Eq6r0GA23h5LrsTB9iIy6iey9Womuq0MWvoovX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\n+723bUJlzPBdtNa7fb/kRfcinbZoF8n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eew5dXIg+VAK9dhg/CXXvOtTMeSh/+ZUrbg35zhJC3FLtNie7qtvYXmWludtJ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\naHuZcMLyOgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 1.70e-01\n", + " final error(valid) = 1.69e-01\n", + " final acc(train) = 9.52e-01\n", + " final acc(valid) = 9.55e-01\n", + " run time per epoch = 10.36\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.50\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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EPx65ryuHL5SR+dkFso5eYnN+CfdEBDJ1QGem9e9EfESQt4d692Jj4f4UavKP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\nGAwGNmzYoHYkzbl16xZZWVlcv34dnU5HcnIyo0e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1aJpGenr6Ze+Y//jjj3n22Wfb/FlBQQH79+/HaDQSHR3NI488cnVXJoQQ1xCl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\nIYRoHyn0fkB5mlH5G1qHqa0sh+5XoT04G801BM1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PnRI3KCiIxsbG71w3KirK93VgYCAe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\n9EIIIYQf+3+DEMdHykKKUgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 1.69e-01\n", + " final error(valid) = 1.71e-01\n", + " final acc(train) = 9.51e-01\n", + " final acc(valid) = 9.52e-01\n", + " run time per epoch = 10.14\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=1.00\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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ECLxmXfK1b98+Xn75ZQzDIDMzk3nz5pGVlUVycjKpqak89dRTFBYW0qlTJ+CL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\n69BvvwnlHug3CMusRdA3pdlH7205QzM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pOe3E6fZiDjMxM91CTnosY5P82061K7TWsG+370j8xGGIt6F++jNUzjxU+LU7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\nUv6p/TXtOIf+6F3qthWgW1t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Tw+rVq7HZbJSXl/PSSy/hcrkwmUwsXbqUGTNmAFBTU8Mf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\ne5w4cQKBQADNzc0ICwtDVlYW5s+fj8jISDQ1NWF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 2.05e-01\n", + " final error(valid) = 2.07e-01\n", + " final acc(train) = 9.40e-01\n", + " final acc(valid) = 9.39e-01\n", + " run time per epoch = 10.99\n" + ] + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "num_epochs = 10 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "learning_rate = 0.2 # learning rate for gradient descent\n", + "\n", + "init_scales = [0.1, 0.2, 0.5, 1.] # scale for random parameter initialisation\n", + "final_errors_train = []\n", + "final_errors_valid = []\n", + "final_accs_train = []\n", + "final_accs_valid = []\n", + "\n", + "for init_scale in init_scales:\n", + "\n", + " print('-' * 80)\n", + " print('learning_rate={0:.2f} init_scale={1:.2f}'\n", + " .format(learning_rate, init_scale))\n", + " print('-' * 80)\n", + " # Reset random number generator and data provider states on each run\n", + " # to ensure reproducibility of results\n", + " rng.seed(seed)\n", + " train_data.reset()\n", + " valid_data.reset()\n", + "\n", + " # Alter data-provider batch size\n", + " train_data.batch_size = batch_size \n", + " valid_data.batch_size = batch_size\n", + "\n", + " # Create a parameter initialiser which will sample random uniform values\n", + " # from [-init_scale, init_scale]\n", + " param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + " # Create a model with two affine layers\n", + " hidden_dim = 100\n", + " model = MultipleLayerModel([\n", + " AffineLayer(input_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, output_dim, param_init, param_init)\n", + " ])\n", + "\n", + " # Initialise a cross entropy error object\n", + " error = CrossEntropySoftmaxError()\n", + "\n", + " # Use a basic gradient descent learning rule\n", + " learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + " stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n", + "\n", + " plt.show()\n", + "\n", + " print(' final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n", + " print(' final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n", + " print(' final acc(train) = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n", + " print(' final acc(valid) = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n", + " print(' run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n", + "\n", + " final_errors_train.append(stats[-1, keys['error(train)']])\n", + " final_errors_valid.append(stats[-1, keys['error(valid)']])\n", + " final_accs_train.append(stats[-1, keys['acc(train)']])\n", + " final_accs_valid.append(stats[-1, keys['acc(valid)']])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n", + "|------------|--------------------|--------------------|------------------|------------------|\n", + "| 0.1 | 1.75e-01 | 1.72e-01 | 0.95 | 0.95 |\n", + "| 0.2 | 1.70e-01 | 1.69e-01 | 0.95 | 0.95 |\n", + "| 0.5 | 1.69e-01 | 1.71e-01 | 0.95 | 0.95 |\n", + "| 1.0 | 2.05e-01 | 2.07e-01 | 0.94 | 0.94 |\n" + ] + } + ], + "source": [ + "j = 0\n", + "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n", + "print('|------------|--------------------|--------------------|------------------|------------------|')\n", + "for init_scale in init_scales:\n", + " print('| {0:.1f} | {1:.2e} | {2:.2e} | {3:.2f} | {4:.2f} |'\n", + " .format(init_scale, \n", + " final_errors_train[j], final_errors_valid[j],\n", + " final_accs_train[j], final_accs_valid[j]))\n", + " j += 1" + ] }, { "cell_type": "markdown", @@ -473,12 +1622,247 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.10\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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HmDx5MsePH+cHP/gBAGPGjLmpwxetp2zxqOV/h3YuR+9xofO34v35E5AyBJPzXhiV3qXL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\nkLsQQggxnJhNGqlxIaTGheCwBdHYOPRN+6YXonk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xZZ1nCn//RCNtbsWouBCemO6ZwsODAmsKh/Ohth2o7dsk1Cb8To/NXNd1Xn31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\nWlrYsGEDDz74IGPHjgUgKiqKlpYW3G4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5EyZMYNasWeh0OgwGw09nFGKokGF2IVSUk5Pz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\nCKFxfwIUz77RSc81WQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 1.86e-01\n", + " final error(valid) = 1.83e-01\n", + " final acc(train) = 9.46e-01\n", + " final acc(valid) = 9.48e-01\n", + " run time per epoch = 13.08\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.20\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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DaQ9E90TNvg81JRUVdOM/UNpKZ8ww0EiG5kmG/hGwT0QTXZPVopgQH8WE+CiK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\njWRc30iKq+rZmedlR14Jz+eeJ+r9i8w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14PYaZFsi+fG0dGbdlEBUkO0Z/ve0txNdvhtdsgUunIEkC+q+h1GzFqBi4gJd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\nn6amJoLBIPPmzSMQCLBs2TJmzpzJxo0bOXTo0A3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 1.74e-01\n", + " final error(valid) = 1.75e-01\n", + " final acc(train) = 9.49e-01\n", + " final acc(valid) = 9.51e-01\n", + " run time per epoch = 15.16\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.50\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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qgWhtFu2UlBTOnDlDWVkZMTExFBUV8dBDD12xjcfj4cUXX+TJJ5/E4XDcXItF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\nwWb01nXgvQT9h/qK99DUNgvqjWTY2Kz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AAVe8bodBbzKZqKur832uq6vDZDJdsM5TTz0FgNPppLS0lMjISI4dO8aRI0co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\nQgjhw/4/zKYGnsCH5MEAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 1.68e-01\n", + " final error(valid) = 1.75e-01\n", + " final acc(train) = 9.50e-01\n", + " final acc(valid) = 9.50e-01\n", + " run time per epoch = 14.17\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=1.00\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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Hs0GqLD6o0ZPRqRPR+Tno9avRL/8GHRWNSp+HGjYGZZbwFkK0f3JNWzTL7dSw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\nrq6murqa/fv3B6bORURE7GR5gnANeRDXvJ9jTZ4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DQXTJZvSG1+DsKeiTibrvF6icqaioy79abgmafHIq1Mj28akG/KYmPTGaxSND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\nmkiXJoQQooP12DDvqrTHjS78G3rTO9D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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 1.98e-01\n", + " final error(valid) = 2.10e-01\n", + " final acc(train) = 9.41e-01\n", + " final acc(valid) = 9.38e-01\n", + " run time per epoch = 16.21\n" + ] + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "num_epochs = 10 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "learning_rate = 0.2 # learning rate for gradient descent\n", + "\n", + "init_scales = [0.1, 0.2, 0.5, 1.] # scale for random parameter initialisation\n", + "final_errors_train = []\n", + "final_errors_valid = []\n", + "final_accs_train = []\n", + "final_accs_valid = []\n", + "\n", + "for init_scale in init_scales:\n", + "\n", + " print('-' * 80)\n", + " print('learning_rate={0:.2f} init_scale={1:.2f}'\n", + " .format(learning_rate, init_scale))\n", + " print('-' * 80)\n", + " # Reset random number generator and data provider states on each run\n", + " # to ensure reproducibility of results\n", + " rng.seed(seed)\n", + " train_data.reset()\n", + " valid_data.reset()\n", + "\n", + " # Alter data-provider batch size\n", + " train_data.batch_size = batch_size \n", + " valid_data.batch_size = batch_size\n", + "\n", + " # Create a parameter initialiser which will sample random uniform values\n", + " # from [-init_scale, init_scale]\n", + " param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + " # Create a model with three affine layers\n", + " hidden_dim = 100\n", + " model = MultipleLayerModel([\n", + " AffineLayer(input_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, output_dim, param_init, param_init)\n", + " ])\n", + "\n", + " # Initialise a cross entropy error object\n", + " error = CrossEntropySoftmaxError()\n", + "\n", + " # Use a basic gradient descent learning rule\n", + " learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + " stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n", + "\n", + " plt.show()\n", + "\n", + " print(' final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n", + " print(' final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n", + " print(' final acc(train) = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n", + " print(' final acc(valid) = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n", + " print(' run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n", + " \n", + " final_errors_train.append(stats[-1, keys['error(train)']])\n", + " final_errors_valid.append(stats[-1, keys['error(valid)']])\n", + " final_accs_train.append(stats[-1, keys['acc(train)']])\n", + " final_accs_valid.append(stats[-1, keys['acc(valid)']])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n", + "|------------|--------------------|--------------------|------------------|------------------|\n", + "| 0.1 | 1.86e-01 | 1.83e-01 | 0.95 | 0.95 |\n", + "| 0.2 | 1.74e-01 | 1.75e-01 | 0.95 | 0.95 |\n", + "| 0.5 | 1.68e-01 | 1.75e-01 | 0.95 | 0.95 |\n", + "| 1.0 | 1.98e-01 | 2.10e-01 | 0.94 | 0.94 |\n" + ] + } + ], + "source": [ + "j = 0\n", + "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n", + "print('|------------|--------------------|--------------------|------------------|------------------|')\n", + "for init_scale in init_scales:\n", + " print('| {0:.1f} | {1:.2e} | {2:.2e} | {3:.2f} | {4:.2f} |'\n", + " .format(init_scale, \n", + " final_errors_train[j], final_errors_valid[j],\n", + " final_accs_train[j], final_accs_valid[j]))\n", + " j += 1" + ] }, { "cell_type": "markdown", @@ -489,12 +1873,249 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.10\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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WR6Qr0FMZDgf8183gycR6+W9YFWXYrr8TIyGpWTm7zeCiYWmcNSCFv322l9c3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\nQzh4wF+u8Q1x8S0C20gPBLc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ZmcmePXtYv349iqKQnp7OvHnzANDpdMyePZunnnoKTdMYPHiwV4tanKF9s8M9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\nsIqKCrZv3+75j6hpGt988w3jx48HIDs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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 2.03e-03\n", + " final error(valid) = 1.35e-01\n", + " final acc(train) = 1.00e+00\n", + " final acc(valid) = 9.73e-01\n", + " run time per epoch = 19.51\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.20\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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6hiaPTZ/rOvUNBh7dwBUWFZQ6yr/unYRSCn59E8bu7ejPzke753GUI77V9ULM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\nGzdSWlpKenp6sKvZpTzzzDMMGTKE6dO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jsC6cHLbX8db5Wbe/itWFXtYUVgWHvAemxzNtaDrnZiczwB2vfdAiIhGm0G6j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\nkS5LRESkWR06tPP2ekmLd9I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Q559/Pg6Hg9jY2FbXKNJeaHhc5Ay47777jrtP2+12Nxm2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\nIiIiUUKhLSIiEiX+H2dunxAXe5sDAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 1.99e-03\n", + " final error(valid) = 1.17e-01\n", + " final acc(train) = 1.00e+00\n", + " final acc(valid) = 9.75e-01\n", + " run time per epoch = 20.58\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=0.50\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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aF8o5xVXkl7nrLBsZYtA+KoT2kSEkRtm896NstI8MISbUIn901iLf1cCQ3eO1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\nI3Tp0oXFixczZsyYgNUmu8dbkbi4ON9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7qy8GdY2+ZoEcCOam+y6v3qy9JdJAiiWC9IRIsrpZSI6NoJc1Bjx1Z1zDmWrx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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 3.07e-03\n", + " final error(valid) = 1.34e-01\n", + " final acc(train) = 1.00e+00\n", + " final acc(valid) = 9.71e-01\n", + " run time per epoch = 20.54\n", + "--------------------------------------------------------------------------------\n", + "learning_rate=0.20 init_scale=1.00\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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/zyW2dzqOC9KCvk273d5gbdqqFXTrUPu5I+VVbD9cyjeHS9ieW8r2vBLWbM73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\nXYS+8XbYt8sK73Ur0Yv/gl7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KYvz48QCkpKQwcOBAfvOb32AYBnfddReGUef3BCGEEEKc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\nRK5CYII=\n", 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206dPb7W+srKSJUuWUFtbS2xsLLm5uVgsFgBuu+02+vXrB4DVauXf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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " final error(train) = 7.72e-03\n", + " final error(valid) = 1.62e-01\n", + " final acc(train) = 1.00e+00\n", + " final acc(valid) = 9.62e-01\n", + " run time per epoch = 21.37\n" + ] + } + ], + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "num_epochs = 100 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "learning_rate = 0.2 # learning rate for gradient descent\n", + "\n", + "init_scales = [0.1, 0.2, 0.5, 1.] # scale for random parameter initialisation\n", + "final_errors_train = []\n", + "final_errors_valid = []\n", + "final_accs_train = []\n", + "final_accs_valid = []\n", + "\n", + "for init_scale in init_scales:\n", + "\n", + " print('-' * 80)\n", + " print('learning_rate={0:.2f} init_scale={1:.2f}'\n", + " .format(learning_rate, init_scale))\n", + " print('-' * 80)\n", + " # Reset random number generator and data provider states on each run\n", + " # to ensure reproducibility of results\n", + " rng.seed(seed)\n", + " train_data.reset()\n", + " valid_data.reset()\n", + "\n", + " # Alter data-provider batch size\n", + " train_data.batch_size = batch_size \n", + " valid_data.batch_size = batch_size\n", + "\n", + " # Create a parameter initialiser which will sample random uniform values\n", + " # from [-init_scale, init_scale]\n", + " param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + " # Create a model with four affine layers\n", + " hidden_dim = 100\n", + " model = MultipleLayerModel([\n", + " AffineLayer(input_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, output_dim, param_init, param_init)\n", + " ])\n", + "\n", + " # Initialise a cross entropy error object\n", + " error = CrossEntropySoftmaxError()\n", + "\n", + " # Use a basic gradient descent learning rule\n", + " learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + " stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n", + "\n", + " plt.show()\n", + "\n", + " print(' final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n", + " print(' final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n", + " print(' final acc(train) = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n", + " print(' final acc(valid) = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n", + " print(' run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n", + "\n", + " final_errors_train.append(stats[-1, keys['error(train)']])\n", + " final_errors_valid.append(stats[-1, keys['error(valid)']])\n", + " final_accs_train.append(stats[-1, keys['acc(train)']])\n", + " final_accs_valid.append(stats[-1, keys['acc(valid)']])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n", + "|------------|--------------------|--------------------|------------------|------------------|\n", + "| 0.1 | 2.03e-03 | 1.35e-01 | 1.00 | 0.97 |\n", + "| 0.2 | 1.99e-03 | 1.17e-01 | 1.00 | 0.97 |\n", + "| 0.5 | 3.07e-03 | 1.34e-01 | 1.00 | 0.97 |\n", + "| 1.0 | 7.72e-03 | 1.62e-01 | 1.00 | 0.96 |\n" + ] + } + ], + "source": [ + "j = 0\n", + "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n", + "print('|------------|--------------------|--------------------|------------------|------------------|')\n", + "for init_scale in init_scales:\n", + " print('| {0:.1f} | {1:.2e} | {2:.2e} | {3:.2f} | {4:.2f} |'\n", + " .format(init_scale, \n", + " final_errors_train[j], final_errors_valid[j],\n", + " final_accs_train[j], final_accs_valid[j]))\n", + " j += 1" + ] }, { "cell_type": "markdown", @@ -506,11 +2127,151 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Set training run hyperparameters\n", + "batch_size = 100 # number of data points in a batch\n", + "num_epochs = 100 # number of training epochs to perform\n", + "stats_interval = 5 # epoch interval between recording and printing stats\n", + "learning_rate = 0.2 # learning rate for gradient descent\n", + "\n", + "init_scales = [0.1, 0.2, 0.5, 1.] # scale for random parameter initialisation\n", + "final_errors_train = []\n", + "final_errors_valid = []\n", + "final_accs_train = []\n", + "final_accs_valid = []\n", + "\n", + "for init_scale in init_scales:\n", + "\n", + " print('-' * 80)\n", + " print('learning_rate={0:.2f} init_scale={1:.2f}'\n", + " .format(learning_rate, init_scale))\n", + " print('-' * 80)\n", + " # Reset random number generator and data provider states on each run\n", + " # to ensure reproducibility of results\n", + " rng.seed(seed)\n", + " train_data.reset()\n", + " valid_data.reset()\n", + "\n", + " # Alter data-provider batch size\n", + " train_data.batch_size = batch_size \n", + " valid_data.batch_size = batch_size\n", + "\n", + " # Create a parameter initialiser which will sample random uniform values\n", + " # from [-init_scale, init_scale]\n", + " param_init = UniformInit(-init_scale, init_scale, rng=rng)\n", + "\n", + " # Create a model with five affine layers\n", + " hidden_dim = 100\n", + " model = MultipleLayerModel([\n", + " AffineLayer(input_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n", + " SigmoidLayer(),\n", + " AffineLayer(hidden_dim, output_dim, param_init, param_init)\n", + " ])\n", + "\n", + " # Initialise a cross entropy error object\n", + " error = CrossEntropySoftmaxError()\n", + "\n", + " # Use a basic gradient descent learning rule\n", + " learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n", + "\n", + " stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n", + " model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n", + "\n", + " plt.show()\n", + "\n", + " print(' final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n", + " print(' final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n", + " print(' final acc(train) = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n", + " print(' final acc(valid) = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n", + " print(' run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n", + "\n", + " final_errors_train.append(stats[-1, keys['error(train)']])\n", + " final_errors_valid.append(stats[-1, keys['error(valid)']])\n", + " final_accs_train.append(stats[-1, keys['acc(train)']])\n", + " final_accs_valid.append(stats[-1, keys['acc(valid)']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "j = 0\n", + "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n", + "print('|------------|--------------------|--------------------|------------------|------------------|')\n", + "for init_scale in init_scales:\n", + " print('| {0:.1f} | {1:.2e} | {2:.2e} | {3:.2f} | {4:.2f} |'\n", + " .format(init_scale, \n", + " final_errors_train[j], final_errors_valid[j],\n", + " final_accs_train[j], final_accs_valid[j]))\n", + " j += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> How does increasing the number of layers affect the model's performance on the training data set? And on the validation data set?\n", + "\n", + "\n", + "The best final training set error across the four initialisation scales used above for each model architecture, consistently decreases as we increase the number of layers.\n", + "\n", + "\n", + "| Number of affine layers | Best final training set error |\n", + "|-------------------------|-------------------------------|\n", + "| 2 | $1.85 \\times 10^{-2}$ |\n", + "| 3 | $5.21 \\times 10^{-3}$ |\n", + "| 4 | $1.99 \\times 10^{-3}$ |\n", + "| 5 | $1.14 \\times 10^{-3}$ |\n", + "\n", + "\n", + "This makes sense as because the number of layers increase, for a fixed hidden layer width, the total number of free parameters in the model increases and so we would expect the model to be able to fit too the training data better.\n", + "\n", + "\n", + "\n", + "If we look at the validation set however we see the opposite trend; as the number of layers increases the best final validation set error increases.\n", + "\n", + "\n", + "| Number of affine layers | Best final validation set error |\n", + "|-------------------------|---------------------------------|\n", + "| 2 | $7.47 \\times 10^{-2}$ |\n", + "| 3 | $8.77 \\times 10^{-2}$ |\n", + "| 4 | $1.17 \\times 10^{-1}$ |\n", + "| 5 | $1.47 \\times 10^{-1}$ |\n", + "\n", + "\n", + "If we look more closely at the training curves for the models with more layers we can see what is happening here. For the models with three or more layers, after a certain number of epochs the validation set error begins to *increase* even as the training set error continues to decrease. This indicates that these models have begun *overfitting* to the training data. We could get a better validation set error in these cases by stopping the training early. *Early stopping* like this is one way of trying to overcome overfitting, in later labs we will consider other methods for improving generalisation by reducing overfitting.\n", + "\n", + "\n", + "> Do deeper models seem to be harder or easier to train (e.g. in terms of ease of choosing training hyperparameters to give good final performance and/or quick convergence)?\n", + "\n", + "> Do the models seem to be sensitive to the choice of the parameter initialisation range? Can you think of any reasons for why setting individual parameter initialisation scales for each AffineLayer in a model might be useful? Can you come up with (or find) any heuristics for setting the parameter initialisation scales?\n", + "\n", + "\n", + "The final performance of the deeper models becomes increasingly sensitive to the choice of parameter initialisation. For the models with two affine layers, the final training errors for initialisation scales 0.1, 0.2 and 0.5 are all within approximately 10% of each other, while for the models with five affine layers there is an approximately 400% increase in final training error if moving from an initialisation scale of 0.2 to 0.1 and a 50% increase in final training error when moving from 0.2 to 0.5. The smaller parameter initialisation scales for the deeper models in particular seem to give poorer initial performance (error curves start from higher values) and for the five affine layer model the smallest parameter initialisation scale run shows a pronounced flatter section at the start of training with around 15 epochs before the error starts significantly decreasing.\n", + "\n", + "\n", + "\n", + "In general the models with more layers also take longer to train per epoch, so on top of issues of potential overfitting and difficulty of choosing parameter initilisations we also need to factor in the potentially slower training of deeper models if computational time is a key constraint.\n", + "\n", + "\n", + "\n", + "We might expect the appropriate initialisation scale for a given affine layer to depend on its input and output dimensionalities. Each output is calculated as the weighted sum of all the inputs, and so for a larger number of inputs the typical magnitude of the output activations will become larger as each will be calculate from a sum over more values. Similarly the backpropagated gradient at each input is calculated as a weighted sum over the gradients at each output, and so for a larger number outputs the typical magnitude of backpropagated gradients will become larger.\n", + "\n", + "\n", + "\n", + "If we wish to keep some measure of the typical magnitude of the activations and backpropagated gradients at a given layer roughly constant through the network then we may therefore wish to set the parameter initialisation in a layer dimensionality dependent way. One heuristic based on trying to achieve a roughly constant variance in activations and backpropagated gradients through the network is to initialise the weights for a layer from a distribution with variance inversely proportional to the sum of the input and output dimensions of the layer. This is sometimes known as the Glorot or Xavier initialisation, after the name of the author of [the paper](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) in which this scheme was proposed. This is discussed in [lecture 4](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp04-learn.pdf).\n", + "" + ] }, { "cell_type": "markdown", @@ -564,10 +2325,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -581,7 +2340,7 @@ "\n", " For inputs `x` and outputs `y` this corresponds to `y = tanh(x)`.\n", " \"\"\"\n", - " raise NotImplementedError('Delete this raise statement and write your code here instead.')\n", + " return np.tanh(inputs)\n", "\n", " def bprop(self, inputs, outputs, grads_wrt_outputs):\n", " \"\"\"Back propagates gradients through a layer.\n", @@ -589,7 +2348,7 @@ " Given gradients with respect to the outputs of the layer calculates the\n", " gradients with respect to the layer inputs.\n", " \"\"\"\n", - " raise NotImplementedError('Delete this raise statement and write your code here instead.')\n", + " return (1. - outputs**2) * grads_wrt_outputs\n", "\n", " def __repr__(self):\n", " return 'TanhLayer'\n", @@ -603,7 +2362,7 @@ "\n", " For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.\n", " \"\"\"\n", - " raise NotImplementedError('Delete this raise statement and write your code here instead.')\n", + " return np.maximum(inputs, 0.)\n", "\n", " def bprop(self, inputs, outputs, grads_wrt_outputs):\n", " \"\"\"Back propagates gradients through a layer.\n", @@ -611,19 +2370,32 @@ " Given gradients with respect to the outputs of the layer calculates the\n", " gradients with respect to the layer inputs.\n", " \"\"\"\n", - " raise NotImplementedError('Delete this raise statement and write your code here instead.')\n", + " return (outputs > 0) * grads_wrt_outputs\n", "\n", " def __repr__(self):\n", " return 'ReluLayer'" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test your implementations by running the cells below." + ] + }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outputs and gradients calculated correctly for TanhLayer.\n" + ] + } + ], "source": [ "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n", "test_grads_wrt_outputs = np.array([[5., 10., -10.], [-5., 0., 10.]])\n", @@ -656,11 +2428,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outputs and gradients calculated correctly for ReluLayer.\n" + ] + } + ], "source": [ "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n", "test_grads_wrt_outputs = np.array([[5., 10., -10.], [-5., 0., 10.]])\n", @@ -686,6 +2464,13 @@ "if all_correct:\n", " print('Outputs and gradients calculated correctly for ReluLayer.')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -694,6 +2479,18 @@ "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, diff --git a/notebooks/04_Generalisation_and_overfitting.ipynb b/notebooks/04_Generalisation_and_overfitting.ipynb new file mode 100644 index 0000000..8bc0f1f --- /dev/null +++ b/notebooks/04_Generalisation_and_overfitting.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generalisation and overfitting\n", + "\n", + "In this notebook we will explore the issue of overfitting and how we can measure how well the models we train generalise their predictions to unseen data. This will build upon the introduction to generalisation given in the [fourth lecture](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp04-learn.pdf)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: overfitting and model complexity in a 1D regression problem\n", + "\n", + "As an exercise we will consider a regression problem. In particular we will attempt to use a multiple layer network model to learn to predict output values from inputs, given a fixed set of (noisy) observations of the underlying functional relationship between inputs and outputs. The aim of the exercise will be to visualise how increasing the complexity of the model we fit to the training data effects the ability of the model to make predictions across the input space.\n", + "\n", + "### Function\n", + "\n", + "To keep things simple we will consider a single input-output function defined by a fourth degree polynomial (quartic)\n", + "\n", + "$$ f(x) = 10 x^4 - 17 x^3 + 8 x^2 - x $$\n", + "\n", + "with the observed values being the function values plus zero-mean Gaussian noise\n", + "\n", + "$$ y = f(x) + 0.01 \\epsilon \\qquad \\epsilon \\sim \\mathcal{N}\\left(\\cdot;\\,0,\\,1\\right) $$\n", + "\n", + "The inputs will be drawn from the uniform distribution on $[0, 1]$.\n", + "\n", + "First import the necessary modules and seed the random number generator by running the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "plt.style.use('ggplot')\n", + "seed = 17102016 \n", + "rng = np.random.RandomState(seed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Write code in the cell below to calculate a polynomial function of one dimensional inputs. \n", + "\n", + "If $\\boldsymbol{c}$ is a length $P$ vector of coefficients corresponding to increasing powers in the polynomial (starting from the constant zero power term up to the $P-1^{\\textrm{th}}$ power) the function should correspond to the following\n", + "\n", + "\\begin{equation}\n", + " f_{\\textrm{polynomial}}(x,\\ \\boldsymbol{c}) = \\sum_{p=0}^{P-1} \\left( c_p x^p \\right)\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def polynomial_function(inputs, coefficients):\n", + " \"\"\"Calculates polynomial with given coefficients of an array of inputs.\n", + " \n", + " Args:\n", + " inputs: One-dimensional array of input values of shape (num_inputs,)\n", + " coefficients: One-dimensional array of polynomial coefficient terms\n", + " with `coefficients[0]` corresponding to the coefficient for the\n", + " zero order term in the polynomial (constant) and `coefficients[-1]`\n", + " corresponding to the highest order term.\n", + " \n", + " Returns:\n", + " One dimensional array of output values of shape (num_inputs,)\n", + " \n", + " \"\"\"\n", + " raise NotImplementedError()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the cell below to test your implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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: " + ] + } + ], + "source": [ + "test_coefficients = np.array([-1., 3., 4.])\n", + "test_inputs = np.array([0., 0.5, 1., 2.])\n", + "test_outputs = np.array([-1., 1.5, 6., 21.])\n", + "assert polynomial_function(test_inputs, test_coefficients).shape == (4,), (\n", + " 'Function gives wrong shape output.'\n", + ")\n", + "assert np.allclose(polynomial_function(test_inputs, test_coefficients), test_outputs), (\n", + " 'Function gives incorrect output values.'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now need to use the random number generator to sample input values and calculate the corresponding target outputs using your polynomial implementation with the relevant coefficients for our function. Do this by running the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "coefficients = np.array([0, -1., 8., -17., 10.])\n", + "input_dim, output_dim = 1, 1\n", + "noise_std = 0.01\n", + "num_data = 80\n", + "inputs = rng.uniform(size=(num_data, input_dim))\n", + "epsilons = rng.normal(size=num_data)\n", + "targets = (polynomial_function(inputs[:, 0], coefficients) + \n", + " epsilons * noise_std)[:, None]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will split the generated data points in to equal sized training and validation data sets and use these to create data provider objects which we can use to train models in our framework. As the dataset is small here we will use a batch size equal to the size of the data set. Run the cell below to split the data and set up the data provider objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlp.data_providers import DataProvider\n", + "num_train = num_data // 2\n", + "batch_size = num_train\n", + "inputs_train, targets_train = inputs[:num_train], targets[:num_train]\n", + "inputs_valid, targets_valid = inputs[num_train:], targets[num_train:]\n", + "train_data = DataProvider(inputs_train, targets_train, batch_size=batch_size, rng=rng)\n", + "valid_data = DataProvider(inputs_valid, targets_valid, batch_size=batch_size, rng=rng)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now visualise the data we will be modelling. Run the cell below to plot the target outputs against inputs for both the training and validation sets. Note the clear underlying smooth functional relationship evident in the noisy data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(8, 4))\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(inputs_train[:, 0], targets_train[:, 0], '.', label='training data')\n", + "ax.plot(inputs_valid[:, 0], targets_valid[:, 0], '.', label='validation data')\n", + "ax.set_xlabel('Inputs $x$', fontsize=14)\n", + "ax.set_ylabel('Ouputs $y$', fontsize=14)\n", + "ax.legend(loc='best')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model\n", + "\n", + "We will fit models with a varying number of parameters to the training data. As multi-layer logistic sigmoid models do not tend to perform well in regressions tasks like this we will instead use a [radial basis function (RBF) network](https://en.wikipedia.org/wiki/Radial_basis_function_network).\n", + "\n", + "This model predicts the output as the weighted sum of basis functions (here Gaussian like bumps) tiled across the input space. The cell below generates a random set of weights and bias for a RBF network and plots the modelled input-output function across inputs $[0, 1]$. Run the cell below for several different number of weight parameters (specified with `num_weights` variable) to get a feel for the sort of predictions the RBF network models produce." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_weights = 15\n", + "weights_scale = 1.\n", + "bias_scale = 1.\n", + "\n", + "def basis_function(x, centre, scale):\n", + " return np.exp(-(x - centre)**2 / scale**2)\n", + "\n", + "weights = rng.normal(size=num_weights) * weights_scale\n", + "bias = rng.normal() * bias_scale\n", + "\n", + "centres = np.linspace(0, 1, weights.shape[0])\n", + "scale = 1. / weights.shape[0]\n", + "\n", + "xs = np.linspace(0, 1, 200)\n", + "ys = np.zeros(xs.shape[0])\n", + "\n", + "fig = plt.figure(figsize=(12, 4))\n", + "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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You do not need to read in to the details of how to implement this model. All of the additional code you need to fit RBF networks is provided in the `RadialBasisFunctionLayer` in the `mlp.layers` module. The `RadialBasisFunctionLayer` class has the same interface as the layer classes we encountered in the previous lab, defining both `fprop` and `bprop` methods, and we can therefore include it as a layer in a `MultipleLayerModel` as with any other layer. \n", + "\n", + "Here we will use the `RadialBasisFunctionLayer` as the first layer in a two layer model. This first layer calculates the basis function terms which are then be weighted and summed together in an `AffineLayer`, the second and final layer. This illustrates the advantage of using a modular modelling framework - we can reuse the code we previously implemented to train a quite different model architecture just by defining a new layer class. \n", + "\n", + "Run the cell below to run some necessary setup code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from mlp.models import MultipleLayerModel\n", + "from mlp.layers import AffineLayer, RadialBasisFunctionLayer\n", + "from mlp.errors import SumOfSquaredDiffsError\n", + "from mlp.initialisers import ConstantInit, UniformInit\n", + "from mlp.learning_rules import GradientDescentLearningRule\n", + "from mlp.optimisers import Optimiser\n", + "\n", + "# Regression problem therefore use sum of squared differences error\n", + "error = SumOfSquaredDiffsError()\n", + "# Use basic gradient descent learning rule with fixed learning rate\n", + "learning_rule = GradientDescentLearningRule(0.1)\n", + "# Initialise weights from uniform distribution and zero bias\n", + "weights_init = UniformInit(-0.1, 0.1)\n", + "biases_init = ConstantInit(0.)\n", + "# Train all models for 2000 epochs\n", + "num_epoch = 2000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The cell below defines RBF network models with varying number of weight parameters (equal to the number of basis functions) and fits each to the training set, recording the final training and validation set errors for the fitted models. Run it now to fit the models and calculate the error values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_weight_list = [2, 5, 10, 25, 50, 100]\n", + "models = []\n", + "train_errors = []\n", + "valid_errors = []\n", + "for num_weight in num_weight_list:\n", + " model = MultipleLayerModel([\n", + " RadialBasisFunctionLayer(num_weight),\n", + " AffineLayer(input_dim * num_weight, output_dim, \n", + " weights_init, biases_init)\n", + " ])\n", + " optimiser = Optimiser(model, error, learning_rule, \n", + " train_data, valid_data)\n", + " print('-' * 80)\n", + " print('Training model with {0} weights'.format(num_weight))\n", + " print('-' * 80)\n", + " _ = optimiser.train(num_epoch, -1)\n", + " outputs_train = model.fprop(inputs_train)[-1]\n", + " outputs_valid = model.fprop(inputs_valid)[-1]\n", + " models.append(model)\n", + " train_errors.append(error(outputs_train, targets_train))\n", + " valid_errors.append(error(outputs_valid, targets_valid))\n", + " print(' Final training set error: {0:.1e}'.format(train_errors[-1]))\n", + " print(' Final validation set error: {0:.1e}'.format(valid_errors[-1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below write code to [plot bar charts](http://matplotlib.org/examples/api/barchart_demo.html) of the training and validation set errors for the different fitted models.\n", + "\n", + "Some questions to think about from the plots:\n", + "\n", + " * Do the models with more free parameters fit the training data better or worse?\n", + " * What does the validation set error value tell us about the models?\n", + " * Of the models fitted here which would you say seems like it is most likely to generalise well to unseen data? \n", + " * Do any of the models seem to be overfitting?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's visualise what the fitted model's predictions look like across the whole input space compared to the 'true' function we were trying to fit. \n", + "\n", + "In the cell below, for each of the fitted models stored in the `models` list above:\n", + " * Compute output predictions for the model across a linearly spaced series of 500 input points between 0 and 1 in the input space.\n", + " * Plot the computed predicted outputs and true function values at the corresponding inputs as line plots on the same axis (use a new axis for each model).\n", + " * On the same axis plot the training data sets input-target pairs as points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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." + ] + } + ], + "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 +}