schuetoliver
/
handson-ml
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
handson-ml/15_processing_sequences_usi...

1300 lines
36 KiB
Plaintext
Raw Blame History

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Chapter 15 – Processing Sequences Using RNNs and CNNs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_This notebook contains all the sample code in chapter 15._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Python ≥3.5 is required\n",
"import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"# TensorFlow ≥2.0-preview is required\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"assert tf.__version__ >= \"2.0\"\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
"import os\n",
"\n",
"# to make this notebook's output stable across runs\n",
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"# To plot pretty figures\n",
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"mpl.rc('axes', labelsize=14)\n",
"mpl.rc('xtick', labelsize=12)\n",
"mpl.rc('ytick', labelsize=12)\n",
"\n",
"# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"rnn\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
"\n",
"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
" print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format=fig_extension, dpi=resolution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic RNNs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate the Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def generate_time_series(batch_size, n_steps):\n",
" freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)\n",
" time = np.linspace(0, 1, n_steps)\n",
" series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10)) # wave 1\n",
" series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20)) # + wave 2\n",
" series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5) # + noise\n",
" return series[..., np.newaxis].astype(np.float32)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"\n",
"n_steps = 50\n",
"series = generate_time_series(10000, n_steps + 1)\n",
"X_train, y_train = series[:7000, :n_steps], series[:7000, -1]\n",
"X_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]\n",
"X_test, y_test = series[9000:, :n_steps], series[9000:, -1]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape, y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def plot_series(series, y=None, y_pred=None, x_label=\"$t$\", y_label=\"$x(t)$\"):\n",
" plt.plot(series, \".-\")\n",
" if y is not None:\n",
" plt.plot(n_steps, y, \"bx\", markersize=10)\n",
" if y_pred is not None:\n",
" plt.plot(n_steps, y_pred, \"ro\")\n",
" plt.grid(True)\n",
" if x_label:\n",
" plt.xlabel(x_label, fontsize=16)\n",
" if y_label:\n",
" plt.ylabel(y_label, fontsize=16, rotation=0)\n",
" plt.hlines(0, 0, 100, linewidth=1)\n",
" plt.axis([0, n_steps + 1, -1, 1])\n",
"\n",
"fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(12, 4))\n",
"for col in range(3):\n",
" plt.sca(axes[col])\n",
" plot_series(X_valid[col, :, 0], y_valid[col, 0],\n",
" y_label=(\"$x(t)$\" if col==0 else None))\n",
"save_fig(\"time_series_plot\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Computing Some Baselines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Naive predictions (just predict the last observed value):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"y_pred = X_valid[:, -1]\n",
"np.mean(keras.losses.mean_squared_error(y_valid, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear predictions:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.Flatten(input_shape=[50, 1]),\n",
" keras.layers.Dense(1)\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\")\n",
"history = model.fit(X_train, y_train, epochs=20,\n",
" validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_valid, y_valid)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def plot_learning_curves(loss, val_loss):\n",
" plt.plot(np.arange(len(loss)) + 0.5, loss, \"b.-\", label=\"Training loss\")\n",
" plt.plot(np.arange(len(val_loss)) + 1, val_loss, \"r.-\", label=\"Validation loss\")\n",
" plt.gca().xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n",
" plt.axis([1, 20, 0, 0.05])\n",
" plt.legend(fontsize=14)\n",
" plt.xlabel(\"Epochs\")\n",
" plt.ylabel(\"Loss\")\n",
" plt.grid(True)\n",
"\n",
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_valid)\n",
"plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using a Simple RNN"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.SimpleRNN(1, input_shape=[None, 1])\n",
"])\n",
"\n",
"optimizer = keras.optimizers.Adam(lr=0.005)\n",
"model.compile(loss=\"mse\", optimizer=optimizer)\n",
"history = model.fit(X_train, y_train, epochs=20,\n",
" validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_valid, y_valid)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_valid)\n",
"plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deep RNNs"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
" keras.layers.SimpleRNN(20, return_sequences=True),\n",
" keras.layers.SimpleRNN(1)\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\")\n",
"history = model.fit(X_train, y_train, epochs=20,\n",
" validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_valid, y_valid)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_valid)\n",
"plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make the second `SimpleRNN` layer return only the last output:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
" keras.layers.SimpleRNN(20),\n",
" keras.layers.Dense(1)\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\")\n",
"history = model.fit(X_train, y_train, epochs=20,\n",
" validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_valid, y_valid)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_valid)\n",
"plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Forecasting Several Steps Ahead"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(43) # not 42, as it would give the first series in the train set\n",
"\n",
"series = generate_time_series(1, n_steps + 10)\n",
"X_new, Y_new = series[:, :n_steps], series[:, n_steps:]\n",
"X = X_new\n",
"for step_ahead in range(10):\n",
" y_pred_one = model.predict(X[:, step_ahead:])[:, np.newaxis, :]\n",
" X = np.concatenate([X, y_pred_one], axis=1)\n",
"\n",
"Y_pred = X[:, n_steps:]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"Y_pred.shape"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def plot_multiple_forecasts(X, Y, Y_pred):\n",
" n_steps = X.shape[1]\n",
" ahead = Y.shape[1]\n",
" plot_series(X[0, :, 0])\n",
" plt.plot(np.arange(n_steps, n_steps + ahead), Y_pred[0, :, 0], \"ro-\")\n",
" plt.plot(np.arange(n_steps, n_steps + ahead), Y[0, :, 0], \"bx-\", markersize=10)\n",
" plt.axis([0, n_steps + ahead, -1, 1])\n",
"\n",
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"save_fig(\"forecast_ahead_plot\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's create an RNN that predicts all 10 next values at once:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"\n",
"n_steps = 50\n",
"series = generate_time_series(10000, n_steps + 10)\n",
"X_train, Y_train = series[:7000, :n_steps], series[:7000, -10:]\n",
"X_valid, Y_valid = series[7000:9000, :n_steps], series[7000:9000, -10:]\n",
"X_test, Y_test = series[9000:, :n_steps], series[9000:, -10:]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
" keras.layers.SimpleRNN(20, return_sequences=True),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(1)),\n",
" keras.layers.Lambda(lambda Y_pred: Y_pred[:, -10:])\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\")\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(43)\n",
"\n",
"series = generate_time_series(1, 50 + 10)\n",
"X_new, Y_new = series[:, :50, :], series[:, -10:, :]\n",
"Y_pred = model.predict(X_new)[:, -10:, :]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's create an RNN that predicts the input sequence, shifted 10 steps into the future. That is, instead of just forecasting time steps 50 to 59 based on time steps 0 to 49, it will forecast time steps 10 to 59 based on time steps 0 to 49 (the time steps 10 to 49 are in the input, but the model is causal so at any time step it cannot see the future inputs):"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"\n",
"n_steps = 50\n",
"series = generate_time_series(10000, n_steps + 10)\n",
"X_train, Y_train = series[:7000, :n_steps], series[:7000, 10:]\n",
"X_valid, Y_valid = series[7000:9000, :n_steps], series[7000:9000, 10:]\n",
"X_test, Y_test = series[9000:, :n_steps], series[9000:, 10:]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"X_train.shape, Y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
" keras.layers.SimpleRNN(20, return_sequences=True),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(1))\n",
"])\n",
"\n",
"def last_10_time_steps_mse(Y_true, Y_pred):\n",
" return keras.metrics.mean_squared_error(Y_true[:, -10:], Y_pred[:, -10:])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_10_time_steps_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(43)\n",
"\n",
"series = generate_time_series(1, 50 + 10)\n",
"X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
"Y_pred = model.predict(X_new)[:, -10:, :]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep RNN with Batch Norm"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
" keras.layers.BatchNormalization(),\n",
" keras.layers.SimpleRNN(20, return_sequences=True),\n",
" keras.layers.BatchNormalization(),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(1))\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_10_time_steps_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep RNNs with Layer Norm"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers.experimental import LayerNormalization"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"class LNSimpleRNNCell(keras.layers.Layer):\n",
" def __init__(self, units, activation=\"tanh\", **kwargs):\n",
" super().__init__(**kwargs)\n",
" self.state_size = units\n",
" self.output_size = units\n",
" self.simple_rnn_cell = keras.layers.SimpleRNNCell(units,\n",
" activation=None)\n",
" self.layer_norm = LayerNormalization()\n",
" self.activation = keras.activations.get(activation)\n",
" def get_initial_state(self, inputs=None, batch_size=None, dtype=None):\n",
" if inputs is not None:\n",
" batch_size = tf.shape(inputs)[0]\n",
" dtype = inputs.dtype\n",
" return [tf.zeros([batch_size, self.state_size], dtype=dtype)]\n",
" def call(self, inputs, states):\n",
" outputs, new_states = self.simple_rnn_cell(inputs, states)\n",
" norm_outputs = self.activation(self.layer_norm(outputs))\n",
" return norm_outputs, [norm_outputs]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.RNN(LNSimpleRNNCell(20), return_sequences=True,\n",
" input_shape=[None, 1]),\n",
" keras.layers.RNN(LNSimpleRNNCell(20), return_sequences=True),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(1))\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_10_time_steps_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating a Custom RNN Class"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"class MyRNN(keras.layers.Layer):\n",
" def __init__(self, cell, return_sequences=False, **kwargs):\n",
" super().__init__(**kwargs)\n",
" self.cell = cell\n",
" self.return_sequences = return_sequences\n",
" self.get_initial_state = getattr(\n",
" self.cell, \"get_initial_state\", self.fallback_initial_state)\n",
" def fallback_initial_state(self, inputs):\n",
" return [tf.zeros([self.cell.state_size], dtype=inputs.dtype)]\n",
" @tf.function\n",
" def call(self, inputs):\n",
" states = self.get_initial_state(inputs)\n",
" n_steps = tf.shape(inputs)[1]\n",
" if self.return_sequences:\n",
" sequences = tf.TensorArray(inputs.dtype, size=n_steps)\n",
" outputs = tf.zeros(shape=[n_steps, self.cell.output_size], dtype=inputs.dtype)\n",
" for step in tf.range(n_steps):\n",
" outputs, states = self.cell(inputs[:, step], states)\n",
" if self.return_sequences:\n",
" sequences = sequences.write(step, outputs)\n",
" if self.return_sequences:\n",
" return sequences.stack()\n",
" else:\n",
" return outputs"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" MyRNN(LNSimpleRNNCell(20), return_sequences=True,\n",
" input_shape=[None, 1]),\n",
" MyRNN(LNSimpleRNNCell(20), return_sequences=True),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(1))\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_10_time_steps_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LSTMs"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.LSTM(20, return_sequences=True, input_shape=[None, 1]),\n",
" keras.layers.LSTM(20, return_sequences=True),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(1))\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_10_time_steps_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_valid, Y_valid)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(43)\n",
"\n",
"series = generate_time_series(1, 50 + 10)\n",
"X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
"Y_pred = model.predict(X_new)[:, -10:, :]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GRUs"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.GRU(20, return_sequences=True, input_shape=[None, 1]),\n",
" keras.layers.GRU(20, return_sequences=True),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(1))\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_10_time_steps_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"model.evaluate(X_valid, Y_valid)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(43)\n",
"\n",
"series = generate_time_series(1, 50 + 10)\n",
"X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
"Y_pred = model.predict(X_new)[:, -10:, :]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using One-Dimensional Convolutional Layers to Process Sequences"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"1D conv layer with kernel size 4, stride 2, VALID padding:\n",
"\n",
" |-----2----| |-----5---... |----23-----|\n",
" |-----1----| |-----4-----| ... |-----22----|\n",
" |-----0----| |-----3----| |---...-21---|\n",
"X: 0 1 2 3 4 5 6 7 8 9 10 11 12 ... 43 44 45 46 47 48 49\n",
"Y: 10 11 12 13 14 15 16 17 18 19 20 21 22 ... 53 54 55 56 57 58 59\n",
"\n",
"Output:\n",
"\n",
"X: 0 1 2 3 4 5 ... 19 20 21 22 23\n",
"Y: 13 15 17 19 21 23 ... 51 53 55 57 59\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"def last_5_time_steps_mse(Y_true, Y_pred):\n",
" return keras.metrics.mean_squared_error(Y_true[:, -5:], Y_pred[:, -5:])\n",
"\n",
"model = keras.models.Sequential([\n",
" keras.layers.Conv1D(filters=20, kernel_size=4, strides=2, padding=\"VALID\",\n",
" input_shape=[None, 1]),\n",
" keras.layers.GRU(20, return_sequences=True),\n",
" keras.layers.GRU(20, return_sequences=True),\n",
" keras.layers.TimeDistributed(keras.layers.Dense(1))\n",
"])\n",
"\n",
"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_5_time_steps_mse])\n",
"history = model.fit(X_train, Y_train[:, 3::2], epochs=20,\n",
" validation_data=(X_valid, Y_valid[:, 3::2]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WaveNet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"C2 /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\.../\\ /\\ /\\ /\\ /\\ /\\ \n",
" / \\ / \\ / \\ / \\ / \\ / \\ / \\ / \\ / \\\n",
" / \\ / \\ / \\ / \\\n",
"C1 /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /.../\\ /\\ /\\ /\\ /\\ /\\ /\\\n",
"X: 0 1 2 3 4 5 6 7 8 9 10 11 12 ... 43 44 45 46 47 48 49\n",
"Y: 10 11 12 13 14 15 16 17 18 19 20 21 22 ... 53 54 55 56 57 58 59\n",
"\n",
"Output:\n",
"\n",
"X: 0 1 2 3 4 5 ... 19 20 21 22 23\n",
"Y: 13 15 17 19 21 23 ... 51 53 55 57 59\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"model = keras.models.Sequential()\n",
"model.add(keras.layers.InputLayer(input_shape=[None, 1]))\n",
"for rate in (1, 2, 4, 8) * 2:\n",
" model.add(keras.layers.Lambda(\n",
" lambda inputs: keras.backend.temporal_padding(inputs, (rate, 0))))\n",
" model.add(keras.layers.Conv1D(filters=20, kernel_size=2, padding=\"VALID\",\n",
" activation=\"relu\", dilation_rate=rate))\n",
"model.add(keras.layers.Conv1D(filters=1, kernel_size=1))\n",
"model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_10_time_steps_mse])\n",
"history = model.fit(X_train, Y_train, epochs=20,\n",
" validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is the original WaveNet defined in the paper: it uses Gated Activation Units instead of ReLU and parametrized skip connections, plus it pads with zeros on the left to avoid getting shorter and shorter sequences:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow import keras\n",
"\n",
"class GatedActivationUnit(keras.layers.Layer):\n",
" def __init__(self, activation=\"tanh\", **kwargs):\n",
" super().__init__(**kwargs)\n",
" self.activation = keras.activations.get(activation)\n",
" def call(self, inputs):\n",
" n_filters = inputs.shape[-1] // 2\n",
" linear_output = self.activation(inputs[..., :n_filters])\n",
" gate = keras.activations.sigmoid(inputs[..., n_filters:])\n",
" return self.activation(linear_output) * gate"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"def wavenet_residual_block(inputs, n_filters, dilation_rate):\n",
" z = keras.backend.temporal_padding(inputs, (dilation_rate, 0))\n",
" z = keras.layers.Conv1D(2 * n_filters, kernel_size=2,\n",
" dilation_rate=dilation_rate)(z)\n",
" z = GatedActivationUnit()(z)\n",
" z = keras.layers.Conv1D(n_filters, kernel_size=1)(z)\n",
" return keras.layers.Add()([z, inputs]), z"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"tf.random.set_seed(42)\n",
"\n",
"n_layers_per_block = 10\n",
"n_blocks = 3\n",
"n_filters = 128\n",
"n_outputs = 256\n",
"\n",
"inputs = keras.layers.Input(shape=[None, 1])\n",
"z = keras.backend.temporal_padding(inputs, (1, 0))\n",
"z = keras.layers.Conv1D(n_filters, kernel_size=2)(z)\n",
"skip_to_last = []\n",
"for dilation_rate in [2**i for i in range(n_layers_per_block)] * n_blocks:\n",
" z, skip = wavenet_residual_block(z, n_filters, dilation_rate)\n",
" skip_to_last.append(skip)\n",
"z = keras.activations.relu(keras.layers.Add()(skip_to_last))\n",
"z = keras.layers.Conv1D(n_filters, kernel_size=1, activation=\"relu\")(z)\n",
"Y_proba = keras.layers.Conv1D(n_outputs, kernel_size=1, activation=\"softmax\")(z)\n",
"\n",
"model = keras.models.Model(inputs=[inputs], outputs=[Y_proba])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
"history = model.fit(X_train, Y_train, epochs=2, validation_data=(X_valid, Y_valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this chapter we explored the fundamentals of RNNs and used them to process sequences (namely, time series). In the process we also looked at other ways to process sequences, including CNNs. In the next chapter we will use RNNs for Natural Language Processing, and we will learn more about RNNs (bidirectional RNNs, stateful vs stateless RNNs, Encoder–Decoders, and Attention-augmented Encoder-Decoders). We will also look at the Transformer, an Attention-only architecture."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Exercise solutions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. to 6."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See Appendix A."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Embedded Reber Grammars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we need to build a function that generates strings based on a grammar. The grammar will be represented as a list of possible transitions for each state. A transition specifies the string to output (or a grammar to generate it) and the next state."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
"\n",
"default_reber_grammar = [\n",
" [(\"B\", 1)], # (state 0) =B=>(state 1)\n",
" [(\"T\", 2), (\"P\", 3)], # (state 1) =T=>(state 2) or =P=>(state 3)\n",
" [(\"S\", 2), (\"X\", 4)], # (state 2) =S=>(state 2) or =X=>(state 4)\n",
" [(\"T\", 3), (\"V\", 5)], # and so on...\n",
" [(\"X\", 3), (\"S\", 6)],\n",
" [(\"P\", 4), (\"V\", 6)],\n",
" [(\"E\", None)]] # (state 6) =E=>(terminal state)\n",
"\n",
"embedded_reber_grammar = [\n",
" [(\"B\", 1)],\n",
" [(\"T\", 2), (\"P\", 3)],\n",
" [(default_reber_grammar, 4)],\n",
" [(default_reber_grammar, 5)],\n",
" [(\"T\", 6)],\n",
" [(\"P\", 6)],\n",
" [(\"E\", None)]]\n",
"\n",
"def generate_string(grammar):\n",
" state = 0\n",
" output = []\n",
" while state is not None:\n",
" index = np.random.randint(len(grammar[state]))\n",
" production, state = grammar[state][index]\n",
" if isinstance(production, list):\n",
" production = generate_string(grammar=production)\n",
" output.append(production)\n",
" return \"\".join(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's generate a few strings based on the default Reber grammar:"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"for _ in range(25):\n",
" print(generate_string(default_reber_grammar), end=\" \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks good. Now let's generate a few strings based on the embedded Reber grammar:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"for _ in range(25):\n",
" print(generate_string(embedded_reber_grammar), end=\" \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, now we need a function to generate strings that do not respect the grammar. We could generate a random string, but the task would be a bit too easy, so instead we will generate a string that respects the grammar, and we will corrupt it by changing just one character:"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"def generate_corrupted_string(grammar, chars=\"BEPSTVX\"):\n",
" good_string = generate_string(grammar)\n",
" index = np.random.randint(len(good_string))\n",
" good_char = good_string[index]\n",
" bad_char = np.random.choice(sorted(set(chars) - set(good_char)))\n",
" return good_string[:index] + bad_char + good_string[index + 1:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at a few corrupted strings:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"for _ in range(25):\n",
" print(generate_corrupted_string(embedded_reber_grammar), end=\" \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To be continued..."
]
}
],
"metadata": {
"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": "3.6.8"
},
"nav_menu": {},
"toc": {
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 6,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Reference in New Issue View Git Blame Copy Permalink