add all files
This commit is contained in:
Sem van der Hoeven
565
course/8 Recurrent Neural Networks.ipynb
Normal file
565
course/8 Recurrent Neural Networks.ipynb
Normal file
@@ -0,0 +1,565 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Recurrent Neural Networks"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"%matplotlib inline\n",
|
||||
"import matplotlib.pyplot as plt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Time series forecasting"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_csv('../data/cansim-0800020-eng-6674700030567901031.csv',\n",
|
||||
" skiprows=6, skipfooter=9,\n",
|
||||
" engine='python')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import MonthEnd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['Adjustments'] = pd.to_datetime(df['Adjustments']) + MonthEnd(1)\n",
|
||||
"df = df.set_index('Adjustments')\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"split_date = pd.Timestamp('01-01-2011')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train = df.loc[:split_date, ['Unadjusted']]\n",
|
||||
"test = df.loc[split_date:, ['Unadjusted']]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ax = train.plot()\n",
|
||||
"test.plot(ax=ax)\n",
|
||||
"plt.legend(['train', 'test'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.preprocessing import MinMaxScaler\n",
|
||||
"\n",
|
||||
"sc = MinMaxScaler()\n",
|
||||
"\n",
|
||||
"train_sc = sc.fit_transform(train)\n",
|
||||
"test_sc = sc.transform(test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_sc[:4]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train = train_sc[:-1]\n",
|
||||
"y_train = train_sc[1:]\n",
|
||||
"\n",
|
||||
"X_test = test_sc[:-1]\n",
|
||||
"y_test = test_sc[1:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Fully connected predictor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tensorflow.keras.models import Sequential\n",
|
||||
"from tensorflow.keras.layers import Dense\n",
|
||||
"import tensorflow.keras.backend as K\n",
|
||||
"from tensorflow.keras.callbacks import EarlyStopping"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"K.clear_session()\n",
|
||||
"\n",
|
||||
"model = Sequential()\n",
|
||||
"model.add(Dense(12, input_dim=1, activation='relu'))\n",
|
||||
"model.add(Dense(1))\n",
|
||||
"model.compile(loss='mean_squared_error', optimizer='adam')\n",
|
||||
"model.summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"early_stop = EarlyStopping(monitor='loss', patience=1, verbose=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.fit(X_train, y_train, epochs=200,\n",
|
||||
" batch_size=2, verbose=1,\n",
|
||||
" callbacks=[early_stop])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred = model.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.plot(y_test)\n",
|
||||
"plt.plot(y_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Recurrent predictor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tensorflow.keras.layers import LSTM"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#3D tensor with shape (batch_size, timesteps, input_dim)\n",
|
||||
"X_train[:, None].shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train_t = X_train[:, None]\n",
|
||||
"X_test_t = X_test[:, None]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"K.clear_session()\n",
|
||||
"model = Sequential()\n",
|
||||
"\n",
|
||||
"model.add(LSTM(6, input_shape=(1, 1)))\n",
|
||||
"\n",
|
||||
"model.add(Dense(1))\n",
|
||||
"\n",
|
||||
"model.compile(loss='mean_squared_error', optimizer='adam')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.fit(X_train_t, y_train,\n",
|
||||
" epochs=100, batch_size=1, verbose=1,\n",
|
||||
" callbacks=[early_stop])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred = model.predict(X_test_t)\n",
|
||||
"plt.plot(y_test)\n",
|
||||
"plt.plot(y_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Windows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_sc.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_sc_df = pd.DataFrame(train_sc, columns=['Scaled'], index=train.index)\n",
|
||||
"test_sc_df = pd.DataFrame(test_sc, columns=['Scaled'], index=test.index)\n",
|
||||
"train_sc_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for s in range(1, 13):\n",
|
||||
" train_sc_df['shift_{}'.format(s)] = train_sc_df['Scaled'].shift(s)\n",
|
||||
" test_sc_df['shift_{}'.format(s)] = test_sc_df['Scaled'].shift(s)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_sc_df.head(13)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train = train_sc_df.dropna().drop('Scaled', axis=1)\n",
|
||||
"y_train = train_sc_df.dropna()[['Scaled']]\n",
|
||||
"\n",
|
||||
"X_test = test_sc_df.dropna().drop('Scaled', axis=1)\n",
|
||||
"y_test = test_sc_df.dropna()[['Scaled']]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train = X_train.values\n",
|
||||
"X_test= X_test.values\n",
|
||||
"\n",
|
||||
"y_train = y_train.values\n",
|
||||
"y_test = y_test.values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Fully Connected on Windows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"K.clear_session()\n",
|
||||
"\n",
|
||||
"model = Sequential()\n",
|
||||
"model.add(Dense(12, input_dim=12, activation='relu'))\n",
|
||||
"model.add(Dense(1))\n",
|
||||
"model.compile(loss='mean_squared_error', optimizer='adam')\n",
|
||||
"model.summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.fit(X_train, y_train, epochs=200,\n",
|
||||
" batch_size=1, verbose=1, callbacks=[early_stop])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred = model.predict(X_test)\n",
|
||||
"plt.plot(y_test)\n",
|
||||
"plt.plot(y_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### LSTM on Windows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train_t = X_train.reshape(X_train.shape[0], 1, 12)\n",
|
||||
"X_test_t = X_test.reshape(X_test.shape[0], 1, 12)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train_t.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"K.clear_session()\n",
|
||||
"model = Sequential()\n",
|
||||
"\n",
|
||||
"model.add(LSTM(6, input_shape=(1, 12)))\n",
|
||||
"\n",
|
||||
"model.add(Dense(1))\n",
|
||||
"\n",
|
||||
"model.compile(loss='mean_squared_error', optimizer='adam')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.summary()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.fit(X_train_t, y_train, epochs=100,\n",
|
||||
" batch_size=1, verbose=1, callbacks=[early_stop])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred = model.predict(X_test_t)\n",
|
||||
"plt.plot(y_test)\n",
|
||||
"plt.plot(y_pred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Exercise 1\n",
|
||||
"\n",
|
||||
"In the model above we reshaped the input shape to: `(num_samples, 1, 12)`, i.e. we treated a window of 12 months as a vector of 12 coordinates that we simultaneously passed to all the LSTM nodes. An alternative way to look at the problem is to reshape the input to `(num_samples, 12, 1)`. This means we consider each input window as a sequence of 12 values that we will pass in sequence to the LSTM. In principle this looks like a more accurate description of our situation. But does it yield better predictions? Let's check it.\n",
|
||||
"\n",
|
||||
"- Reshape `X_train` and `X_test` so that they represent a set of univariate sequences\n",
|
||||
"- retrain the same LSTM(6) model, you'll have to adapt the `input_shape`\n",
|
||||
"- check the performance of this new model, is it better at predicting the test data?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"## Exercise 2\n",
|
||||
"\n",
|
||||
"RNN models can be applied to images too. In general we can apply them to any data where there's a connnection between nearby units. Let's see how we can easily build a model that works with images.\n",
|
||||
"\n",
|
||||
"- Load the MNIST data, by now you should be able to do it blindfolded :)\n",
|
||||
"- reshape it so that an image looks like a long sequence of pixels\n",
|
||||
"- create a recurrent model and train it on the training data\n",
|
||||
"- how does it perform compared to a fully connected? How does it compare to Convolutional Neural Networks?\n",
|
||||
"\n",
|
||||
"(feel free to run this exercise on a cloud GPU if it's too slow on your laptop)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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": "3.7.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user