{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 1 - a pitch detection network\n", "\n", "In this notebook, we wlll train a single-layer network for a super easy task. The goal is to show\n", "how Dense layer is working.\n", "\n", "We will use a synthesized data here. It consists of a pure sinusoid on 12 differnt pitches. Then we will compute a CQT and feed one of the CQT frames into the network." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import librosa\n", "import keras\n", "from future.utils import implements_iterator # for python 2 compatibility for __next__()\n", "from matplotlib import pyplot as plt\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "plt.rc('figure', titlesize=20) \n", "plt.rc('font', size=20)\n", "plt.rc('xtick', labelsize=12) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions to generate data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sin_wave(secs, freq, sr, gain):\n", " '''\n", " Generates a sine wave of frequency given by freq, with duration of secs.\n", " '''\n", " t = np.arange(sr * secs)\n", " return gain * np.sin(2 * np.pi * freq * t / sr)\n", "\n", "def whitenoise(gain, shape):\n", " '''\n", " Generates white noise of duration given by secs\n", " '''\n", " return gain * np.random.uniform(-1., 1., shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A class to generate data batches" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class DataGen:\n", " def __init__(self, sr=16000, batch_size=128):\n", " np.random.seed(1209)\n", " self.pitches = [440., 466.2, 493.8, 523.3, 554.4, 587.3,\n", " 622.3, 659.3, 698.5, 740., 784.0, 830.6]\n", "\n", " self.sr = sr\n", " self.n_class = len(self.pitches) # 12 pitches\n", " self.secs = 1.\n", " self.batch_size = batch_size\n", " self.sins = []\n", " self.labels = np.eye(self.n_class)[range(0, self.n_class)] # 1-hot-vectors\n", "\n", " for freq in self.pitches:\n", " cqt = librosa.cqt(sin_wave(self.secs, freq, self.sr, gain=0.5), sr=sr,\n", " fmin=220, n_bins=36, filter_scale=2)[:, 1] # use only one frame!\n", " cqt = librosa.amplitude_to_db(cqt, ref=np.min)\n", " cqt = cqt / np.max(cqt)\n", " self.sins.append(cqt)\n", "\n", " self.cqt_shape = cqt.shape # (36, )\n", "\n", " def __next__(self):\n", " choice = np.random.choice(12, size=self.batch_size, # pick pitches for this batch\n", " replace=True)\n", " noise_gain = 0.1 * np.random.random_sample(1) # a random noise gain \n", " noise = whitenoise(noise_gain, self.cqt_shape) # generate white noise\n", " xs = [noise + self.sins[i] for i in choice] # compose a batch with additive noise\n", " ys = [self.labels[i] for i in choice] # corresponding labels\n", "\n", " return np.array(xs, dtype=np.float32), np.array(ys, dtype=np.float32)\n", "\n", " next = __next__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A quick look on the data we're generating -- and we'll feed as inputs" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input: A frame of CQT in a shape of: (36,)\n", "Input batch: CQT frames, (128, 36)\n", "Number of classes (pitches): 12\n", "\n" ] }, { "data": { "image/png": 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p2I7yG8Z9+/zp8+Ex+nQ6RSSJbM368rWC2yvsuTif7MHXWYbrs715PWP5DPeH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\nD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datagen = DataGen()\n", "print(\"Input: A frame of CQT in a shape of: {}\".format(datagen.cqt_shape))\n", "x, y = next(datagen)\n", "print(\"Input batch: CQT frames, {}\".format(x.shape))\n", "print(\"Number of classes (pitches): {}\".format(datagen.n_class))\n", "plt.figure(figsize=(20, 6))\n", "\n", "for i in range(2):\n", " x, y = next(datagen)\n", " plt.subplot(2, 2, i+1)\n", " plt.imshow(x.transpose(), cmap=plt.get_cmap('Blues'))\n", " plt.xlabel('data sample index')\n", " plt.ylabel('pitch index')\n", " plt.title('Batch {} (x, input)'.format(i+1))\n", " plt.subplot(2, 2, i+3)\n", " plt.imshow(y.transpose(), cmap=plt.get_cmap('Blues'))\n", " plt.title('Batch {} (y, label)'.format(i+1))\n", "\n", "print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each subplot is a visualisation of each batch. The pitch ranges only in [440 Hz, 830 Hz] (A4 - G#5) but the CQT covers wider range of frequencies (3 octaves from 220 Hz to 880 Hz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build a model!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "val_datagen = DataGen() # this is a generator for validation set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model is very simple. No bias, only single dense layer, which will connect a 36-dim input to a 12-dim output." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "model = keras.models.Sequential()\n", "model.add(keras.layers.Dense(datagen.n_class, use_bias=False,\n", " input_shape=datagen.cqt_shape)) # A dense layer (36 input nodes --> 12 output nodes)\n", "model.add(keras.layers.Activation('softmax')) # Softmax because it's single-label classification\n", "\n", "model.compile(optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.9, # a pretty standard optimizer\n", " decay=1e-6, nesterov=True),\n", " loss='categorical_crossentropy', # categorical crossentropy makes sense with Softmax\n", " metrics=['accuracy']) # we'll also measure the performance but it's NOT a loss function " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Number of parameters => $432 = 36 \\times 12 $. If there is bias, then it would become $36 \\times 12 + 12$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_1 (Dense) (None, 12) 432 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 12) 0 \n", "=================================================================\n", "Total params: 432\n", "Trainable params: 432\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary() # Let's see the network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train it!\n", "Alright, let's train it!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/25\n", "200/200 [==============================] - 0s - loss: 2.1811 - acc: 0.5381 - val_loss: 1.8601 - val_acc: 0.9258\n", "Epoch 2/25\n", "200/200 [==============================] - 0s - loss: 1.6234 - acc: 0.9729 - val_loss: 1.4143 - val_acc: 1.0000\n", "Epoch 3/25\n", "200/200 [==============================] - 0s - loss: 1.2387 - acc: 1.0000 - val_loss: 1.0857 - val_acc: 1.0000\n", "Epoch 4/25\n", "200/200 [==============================] - 0s - loss: 0.9700 - acc: 1.0000 - val_loss: 0.8587 - val_acc: 1.0000\n", "Epoch 5/25\n", "200/200 [==============================] - 0s - loss: 0.7810 - acc: 1.0000 - val_loss: 0.7107 - val_acc: 1.0000\n", "Epoch 6/25\n", "200/200 [==============================] - 0s - loss: 0.6424 - acc: 1.0000 - val_loss: 0.5827 - val_acc: 1.0000\n", "Epoch 7/25\n", "200/200 [==============================] - 0s - loss: 0.5405 - acc: 1.0000 - val_loss: 0.5087 - val_acc: 1.0000\n", "Epoch 8/25\n", "200/200 [==============================] - 0s - loss: 0.4618 - acc: 1.0000 - val_loss: 0.4241 - val_acc: 1.0000\n", "Epoch 9/25\n", "200/200 [==============================] - 0s - loss: 0.4014 - acc: 1.0000 - val_loss: 0.3822 - val_acc: 1.0000\n", "Epoch 10/25\n", "200/200 [==============================] - 0s - loss: 0.3546 - acc: 1.0000 - val_loss: 0.3289 - val_acc: 1.0000\n", "Epoch 11/25\n", "200/200 [==============================] - 0s - loss: 0.3149 - acc: 1.0000 - val_loss: 0.3019 - val_acc: 1.0000\n", "Epoch 12/25\n", "200/200 [==============================] - 0s - loss: 0.2843 - acc: 1.0000 - val_loss: 0.2739 - val_acc: 1.0000\n", "Epoch 13/25\n", "200/200 [==============================] - 0s - loss: 0.2566 - acc: 1.0000 - val_loss: 0.2361 - val_acc: 1.0000\n", "Epoch 14/25\n", "200/200 [==============================] - 0s - loss: 0.2353 - acc: 1.0000 - val_loss: 0.2244 - val_acc: 1.0000\n", "Epoch 15/25\n", "200/200 [==============================] - 0s - loss: 0.2161 - acc: 1.0000 - val_loss: 0.2038 - val_acc: 1.0000\n", "Epoch 16/25\n", "200/200 [==============================] - 0s - loss: 0.2011 - acc: 1.0000 - val_loss: 0.1935 - val_acc: 1.0000\n", "Epoch 17/25\n", "200/200 [==============================] - 0s - loss: 0.1860 - acc: 1.0000 - val_loss: 0.1778 - val_acc: 1.0000\n", "Epoch 18/25\n", "200/200 [==============================] - 0s - loss: 0.1737 - acc: 1.0000 - val_loss: 0.1700 - val_acc: 1.0000\n", "Epoch 19/25\n", "200/200 [==============================] - 0s - loss: 0.1629 - acc: 1.0000 - val_loss: 0.1587 - val_acc: 1.0000\n", "Epoch 20/25\n", "200/200 [==============================] - 0s - loss: 0.1535 - acc: 1.0000 - val_loss: 0.1450 - val_acc: 1.0000\n", "Epoch 21/25\n", "200/200 [==============================] - 0s - loss: 0.1451 - acc: 1.0000 - val_loss: 0.1409 - val_acc: 1.0000\n", "Epoch 22/25\n", "200/200 [==============================] - 0s - loss: 0.1369 - acc: 1.0000 - val_loss: 0.1318 - val_acc: 1.0000\n", "Epoch 23/25\n", "200/200 [==============================] - 0s - loss: 0.1307 - acc: 1.0000 - val_loss: 0.1275 - val_acc: 1.0000\n", "Epoch 24/25\n", "200/200 [==============================] - 0s - loss: 0.1238 - acc: 1.0000 - val_loss: 0.1163 - val_acc: 1.0000\n", "Epoch 25/25\n", "200/200 [==============================] - 0s - loss: 0.1177 - acc: 1.0000 - val_loss: 0.1202 - val_acc: 1.0000\n" ] } ], "source": [ "history = model.fit_generator(datagen, steps_per_epoch=200, epochs=25, verbose=1,\n", " validation_data=val_datagen, validation_steps=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What happened?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NdBSRRkgJdhVy8os0yVFEpDFo1R5atoEdlcdhZ3ZOZvlXBygucVVcKCISOkqw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\nXwOWVLu+Zxljrx9XBu/jxcnynno5n6eLsZ7O5Xn4ParNrtG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 4))\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['acc'], label='training')\n", "plt.plot(history.history['val_acc'], label='validation', alpha=0.7)\n", "plt.title('Accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend()\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['loss'], label='training')\n", "plt.plot(history.history['val_loss'], label='validation', alpha=0.7)\n", "plt.title('Loss')\n", "plt.xlabel('epoch')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Validation set loss is similar to training loss, i.e., no overfitting.\n", "\n", "### How about on test set?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loss: 0.113415751606, accuracy: 1.0\n" ] } ], "source": [ "loss = model.evaluate_generator(datagen, steps=10)\n", "print(\"loss: {}, accuracy: {}\".format(loss[0], loss[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How does the network work after training?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(36, 12)\n" ] } ], "source": [ "weights = model.get_weights()[0] # (36, 12)\n", "print(weights.shape)\n", "# weights = weights / np.sum(np.abs(weights), axis=1, keepdims=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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cCXgBMAS8JiLuKXXymoFPRABHAlNJSQEi4lpJvwb2lbRzRFzf5JjjgK3z10eT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rcuFdkTP7ETBY4cbhwGM5u8vwCZuV+fQYQj6/NPf+A7l8sVvOTmlc1s3XLfp9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\nwDd1jdxewBn4Kvs6wB2SvgM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DYgAfBEEQBEEQBEEQBOOAGMAHQRAEQRAEQRAEwTggBvBB\nEARBEARBEARBMA6IAXwQBEEQBEEQBEEQjANiAB8EQRAEQRAEQRAE44AYwAdBEARBEARBEATBOCAG\n8EEQBEEQBEEQBEEwDvj/AR2abmroW+8VAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# weights\n", "plt.figure(figsize=(20, 5))\n", "plt.imshow(weights.transpose(), cmap=plt.get_cmap('Blues'))\n", "plt.colorbar()\n", "plt.title('Visualisation of the trained weights ($W$)')\n", "pitch_names = 'A4 A#4 B4 C5 C#5 D5 D#5 E5 F5 F#5 G5 G#5'.split(' ')\n", "plt.yticks(range(0, 12), ['{} ({})'.format(p, str(i)) for p, i in zip(pitch_names, range(1, 13))])\n", "plt.xticks(range(0, 36), [str(i) for i in range(1, 37)])\n", "plt.xlabel('Frequency bands of CQT')\n", "plt.ylabel('output')\n", "# an example input\n", "plt.figure(figsize=(20, 1))\n", "plt.imshow(x[0:1], cmap=plt.get_cmap('Blues'))\n", "plt.xticks(range(0, 36), [str(i) for i in range(1, 37)])\n", "plt.colorbar()\n", "plt.title('What would be the output if this CQT frame is multiplied to this weights?')\n", "plt.yticks([])\n", "print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is visualisation of weights $\\textbf{W}$.\n", "\n", "Each row corresponeds to each output node. For example, the 1st row (A4) is connected to the 1st output node, which will be activated (has a large value) if the network thinks it is A4.\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimated pitch: F5\n", "Groundtruth: F5\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def softmax(x):\n", " \"\"\"A softmax function that is not perfect in terms of numerical stability (it might overflow)\"\"\"\n", " return np.exp(x) / np.exp(x).sum(axis=0)\n", "\n", "output_for_x = softmax(np.dot(weights.transpose(), x[0]))\n", "plt.bar(range(len(output_for_x)), output_for_x)\n", "plt.xticks(range(len(output_for_x)), pitch_names)\n", "plt.title('Output node values for x[0]: ')\n", "print('Estimated pitch: {}'.format(pitch_names[np.argmax(output_for_x)]))\n", "print('Groundtruth: {}'.format(pitch_names[np.argmax(y[0])]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }