An RNN model for temperature data

This time we will be working with real data: daily (Tmin, Tmax) temperature series from 36 weather stations spanning 50 years. It is to be noted that a pretty good predictor model already exists for temperatures: the average of temperatures on the same day of the year in N previous years. It is not clear if RNNs can do better but we will se how far they can go.

In [16]:
import math
import sys
import time
import numpy as np

sys.path.insert(0, 'temperatures/utils/') #so python can find the utils_ modules
import utils_batching
import utils_args

import tensorflow as tf
from import file_io as gfile
print("Tensorflow version: " + tf.__version__)
Tensorflow version: 1.8.0
In [2]:
from matplotlib import pyplot as plt
import utils_prettystyle
import utils_display

Download Data

In [ ]:
gsutil -m cp gs://cloud-training-demos/courses/machine_learning/deepdive/09_sequence/temperatures/* $DOWNLOAD_DIR


N_FORWARD = 1: works but model struggles to predict from some positions
N_FORWARD = 4: better but still bad occasionnally
N_FORWARD = 8: works perfectly

In [13]:
NB_EPOCHS = 5       # number of times the model sees all the data during training

N_FORWARD = 8       # train the network to predict N in advance (traditionnally 1)
RESAMPLE_BY = 5     # averaging period in days (training on daily data is too much)
RNN_CELLSIZE = 128  # size of the RNN cells
N_LAYERS = 2        # number of stacked RNN cells (needed for tensor shapes but code must be changed manually)
SEQLEN = 128        # unrolled sequence length
BATCHSIZE = 64      # mini-batch size
DROPOUT_PKEEP = 0.7 # probability of neurons not being dropped (should be between 0.5 and 1)
ACTIVATION = tf.nn.tanh # Activation function for GRU cells (tf.nn.relu or tf.nn.tanh)

JOB_DIR  = "checkpoints"
DATA_DIR = "temperatures/data"

# potentially override some settings from command-line arguments
if __name__ == '__main__':
    JOB_DIR, DATA_DIR = utils_args.read_args1(JOB_DIR, DATA_DIR)

ALL_FILEPATTERN = DATA_DIR + "/*.csv" # pattern matches all 1666 files  
EVAL_FILEPATTERN = DATA_DIR + "/USC000*2.csv" # pattern matches 8 files
# pattern USW*.csv -> 298 files, pattern USW*0.csv -> 28 files
print('Reading data from "{}".\nWrinting checkpoints to "{}".'.format(DATA_DIR, JOB_DIR))
Reading data from "temperatures/data".
Wrinting checkpoints to "checkpoints".

Temperature data

This is what our temperature datasets looks like: sequences of daily (Tmin, Tmax) from 1960 to 2010. They have been cleaned up and eventual missing values have been filled by interpolation. Interpolated regions of the dataset are marked in red on the graph.

In [14]:
all_filenames = gfile.get_matching_files(ALL_FILEPATTERN)
eval_filenames = gfile.get_matching_files(EVAL_FILEPATTERN)
train_filenames = list(set(all_filenames) - set(eval_filenames))

# By default, this utility function loads all the files and places data
# from them as-is in an array, one file per line. Later, we will use it
# to shape the dataset as needed for training.
ite = utils_batching.rnn_multistation_sampling_temperature_sequencer(eval_filenames)
evtemps, _, evdates, _, _ = next(ite) # gets everything

print('Pattern "{}" matches {} files'.format(ALL_FILEPATTERN, len(all_filenames)))
print('Pattern "{}" matches {} files'.format(EVAL_FILEPATTERN, len(eval_filenames)))
print("Evaluation files: {}".format(len(eval_filenames)))
print("Training files: {}".format(len(train_filenames)))
print("Initial shape of the evaluation dataset: " + str(evtemps.shape))
print("{} files, {} data points per file, {} values per data point"
      " (Tmin, Tmax, is_interpolated) ".format(evtemps.shape[0], evtemps.shape[1],evtemps.shape[2]))
Loading 8 files........
Pattern "temperatures/data/*.csv" matches 36 files
Pattern "temperatures/data/USC000*2.csv" matches 8 files
Evaluation files: 8
Training files: 28
Initial shape of the evaluation dataset: (8, 18262, 3)
8 files, 18262 data points per file, 3 values per data point (Tmin, Tmax, is_interpolated) 
In [15]:
# You can adjust the visualisation range and dataset here.
# Interpolated regions of the dataset are marked in red.
WEATHER_STATION = 0 # 0 to 7 in default eval dataset
START_DATE = 0      # 0 = Jan 2nd 1950
END_DATE = 18262    # 18262 = Dec 31st 2009
visu_temperatures = evtemps[WEATHER_STATION,START_DATE:END_DATE]
visu_dates = evdates[START_DATE:END_DATE]

utils_display.picture_this_4(visu_temperatures, visu_dates)
/usr/local/envs/py3env/lib/python3.5/site-packages/matplotlib/ UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))


Our RNN would need ot be unrolled across 365 steps to capture the yearly temperature cycles. That's a bit too much. We will resample the temparatures and work with 5-day averages for example. This is what resampled (Tmin, Tmax) temperatures look like.

In [10]:
# This time we ask the utility function to average temperatures over 5-day periods (RESAMPLE_BY=5)
ite = utils_batching.rnn_multistation_sampling_temperature_sequencer(eval_filenames, RESAMPLE_BY, tminmax=True)
evaltemps, _, evaldates, _, _ = next(ite)
Loading 8 files........
In [11]:
# display five years worth of data
WEATHER_STATION = 0              # 0 to 7 in default eval dataset
START_DATE = 0                   # 0 = Jan 2nd 1950
END_DATE = 365*5//RESAMPLE_BY    # 5 years
visu_temperatures = evaltemps[WEATHER_STATION, START_DATE:END_DATE]
visu_dates = evaldates[START_DATE:END_DATE]
plt.fill_between(visu_dates, visu_temperatures[:,0], visu_temperatures[:,1])
/usr/local/envs/py3env/lib/python3.5/site-packages/matplotlib/ UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))

Visualize training sequences

This is what the neural network will see during training.

In [12]:
# The function rnn_multistation_sampling_temperature_sequencer puts one weather station per line in
# a batch and continues with data from the same station in corresponding lines in the next batch.
# Features and labels are returned with shapes [BATCHSIZE, SEQLEN, 2]. The last dimension of size 2
# contains (Tmin, Tmax).
ite = utils_batching.rnn_multistation_sampling_temperature_sequencer(eval_filenames,

# load 6 training sequences (each one contains data for all weather stations)
visu_data = [next(ite) for _ in range(6)]
Loading 8 files........
In [13]:
# Check that consecutive training sequences from the same weather station are indeed consecutive
utils_display.picture_this_5(visu_data, WEATHER_STATION)
/usr/local/envs/py3env/lib/python3.5/site-packages/matplotlib/ UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))

The model definition

deep RNN schematic

When executed, this function instantiates the Tensorflow graph for our model.

In [14]:
def model_rnn_fn(features, Hin, labels, step, dropout_pkeep):
    X = features  # shape [BATCHSIZE, SEQLEN, 2], 2 for (Tmin, Tmax)
    batchsize = tf.shape(X)[0]
    seqlen = tf.shape(X)[1]
    pairlen = tf.shape(X)[2] # should be 2 (tmin, tmax)
    cells = [tf.nn.rnn_cell.GRUCell(RNN_CELLSIZE, activation=ACTIVATION) for _ in range(N_LAYERS)]
    # dropout useful between cell layers only: no output dropout on last cell
    cells = [tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob = dropout_pkeep) for cell in cells]
    # a stacked RNN cell still works like an RNN cell
    cell = tf.nn.rnn_cell.MultiRNNCell(cells, state_is_tuple=False)
    # the sequence unrolling happens here
    Yn, H = tf.nn.dynamic_rnn(cell, X, initial_state=Hin, dtype=tf.float32)
    Yn = tf.reshape(Yn, [batchsize*seqlen, RNN_CELLSIZE])
    Yr = tf.layers.dense(Yn, 2) # Yr [BATCHSIZE*SEQLEN, 2]
    Yr = tf.reshape(Yr, [batchsize, seqlen, 2]) # Yr [BATCHSIZE, SEQLEN, 2]
    Yout = Yr[:,-N_FORWARD:,:] # Last N_FORWARD outputs Yout [BATCHSIZE, N_FORWARD, 2]
    loss = tf.losses.mean_squared_error(Yr, labels) # labels[BATCHSIZE, SEQLEN, 2]
    lr = 0.001 + tf.train.exponential_decay(0.01, step, 1000, 0.5)
    optimizer = tf.train.AdamOptimizer(learning_rate=lr)
    train_op = optimizer.minimize(loss)
    return Yout, H, loss, train_op, Yr

Instantiate the model

In [15]:
tf.reset_default_graph() # restart model graph from scratch

# placeholder for inputs
Hin = tf.placeholder(tf.float32, [None, RNN_CELLSIZE * N_LAYERS])
features = tf.placeholder(tf.float32, [None, None, 2]) # [BATCHSIZE, SEQLEN, 2]
labels = tf.placeholder(tf.float32, [None, None, 2]) # [BATCHSIZE, SEQLEN, 2]
step = tf.placeholder(tf.int32)
dropout_pkeep = tf.placeholder(tf.float32)

# instantiate the model
Yout, H, loss, train_op, Yr = model_rnn_fn(features, Hin, labels, step, dropout_pkeep)

Initialize Tensorflow session

This resets all neuron weights and biases to initial random values

In [16]:
# variable initialization
sess = tf.Session()
init = tf.global_variables_initializer()[init])
saver = tf.train.Saver(max_to_keep=1)

The training loop

You can re-execute this cell to continue training.

Training data must be batched correctly, one weather station per line, continued on the same line across batches. This way, output states computed from one batch are the correct input states for the next batch. The provided utility function rnn_multistation_sampling_temperature_sequencer does the right thing. batching for RNNs

In [25]:
losses = []
indices = []
last_epoch = 99999
last_fileid = 99999

for i, (next_features, next_labels, dates, epoch, fileid) in enumerate(
                                                                   NB_EPOCHS, tminmax=True)):
    # reinintialize state between epochs or when starting on data from a new weather station
    if epoch != last_epoch or fileid != last_fileid:
        batchsize = next_features.shape[0]
        H_ = np.zeros([batchsize, RNN_CELLSIZE * N_LAYERS])
        print("State reset")

    feed = {Hin: H_, features: next_features, labels: next_labels, step: i, dropout_pkeep: DROPOUT_PKEEP}
    Yout_, H_, loss_, _, Yr_ =[Yout, H, loss, train_op, Yr], feed_dict=feed)
    # print progress
    if i%20 == 0:
        print("{}: epoch {} loss = {} ({} weather stations this epoch)".format(i, epoch, np.mean(loss_), fileid+1))
    if i%10 == 0:
    last_epoch = epoch
    last_fileid = fileid
# save the trained model
SAVEDMODEL = JOB_DIR + "/ckpt" + str(int(time.time()))
tf.saved_model.simple_save(sess, SAVEDMODEL,
                           inputs={"features":features, "Hin":Hin, "dropout_pkeep":dropout_pkeep},
                           outputs={"Yout":Yout, "H":H})
Loading 28 files............................
State reset
0: epoch 0 loss = 23.13544273376465 (28 weather stations this epoch)
20: epoch 0 loss = 20.487436294555664 (28 weather stations this epoch)
State reset
40: epoch 1 loss = 17.833250045776367 (28 weather stations this epoch)
State reset
60: epoch 2 loss = 19.47975730895996 (28 weather stations this epoch)
80: epoch 2 loss = 21.87295913696289 (28 weather stations this epoch)
State reset
100: epoch 3 loss = 21.765390396118164 (28 weather stations this epoch)
State reset
120: epoch 4 loss = 19.88873863220215 (28 weather stations this epoch)
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: b'checkpoints/ckpt1535575100/saved_model.pb'
In [18]:
plt.ylim(ymax=np.amax(losses[1:])) # ignore first value for scaling
plt.plot(indices, losses)
/usr/local/envs/py3env/lib/python3.5/site-packages/matplotlib/ UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))


This is a generative model: run an trained RNN cell in a loop

In [19]:
def prediction_run(predict_fn, prime_data, run_length):
    H = np.zeros([1, RNN_CELLSIZE * N_LAYERS]) # zero state initially
    Yout = np.zeros([1, N_FORWARD, 2])
    data_len = prime_data.shape[0]-N_FORWARD

    # prime the state from data
    if data_len > 0:
        Yin = np.array(prime_data[:-N_FORWARD])
        Yin = np.reshape(Yin, [1, data_len, 2]) # reshape as one sequence of pairs (Tmin, Tmax)
        r = predict_fn({'features': Yin, 'Hin':H, 'dropout_pkeep':1.0}) # no dropout during inference
        Yout = r["Yout"]
        H = r["H"]
        # initaily, put real data on the inputs, not predictions
        Yout = np.expand_dims(prime_data[-N_FORWARD:], axis=0)
        # Yout shape [1, N_FORWARD, 2]: batch of a single sequence of length N_FORWARD of (Tmin, Tmax) data pointa
    # run prediction
    # To generate a sequence, run a trained cell in a loop passing as input and input state
    # respectively the output and output state from the previous iteration.
    results = []
    for i in range(run_length//N_FORWARD+1):
        r = predict_fn({'features': Yout, 'Hin':H, 'dropout_pkeep':1.0}) # no dropout during inference
        Yout = r["Yout"]
        H = r["H"]
        results.append(Yout[0]) # shape [N_FORWARD, 2]
    return np.concatenate(results, axis=0)[:run_length]


In [20]:

# Try starting predictions from January / March / July (resp. OFFSET = YEAR or YEAR+QYEAR or YEAR+2*QYEAR)
# Some start dates are more challenging for the model than others.

RMSELEN=3*365//(RESAMPLE_BY*2) # accuracy of predictions 1.5 years in advance
In [21]:
# Restore the model from the last checkpoint saved previously.

# Alternative checkpoints:
# Once you have trained on all 1666 weather stations on Google Cloud ML Engine, you can load the checkpoint from there.
# SAVEDMODEL = "gs://{BUCKET}/sinejobs/sines_XXXXXX_XXXXXX/ckptXXXXXXXX"
# A sample checkpoint is provided with the lab. You can try loading it for comparison.
# SAVEDMODEL = "temperatures_best_checkpoint"

predict_fn = tf.contrib.predictor.from_saved_model(SAVEDMODEL)
INFO:tensorflow:Restoring parameters from b'checkpoints/ckpt1535574817/variables/variables'
In [22]:
for evaldata in evaltemps:
    prime_data = evaldata[OFFSET:OFFSET+PRIMELEN]
    results = prediction_run(predict_fn, prime_data, RUNLEN)
    utils_display.picture_this_6(evaldata, evaldates, prime_data, results, PRIMELEN, RUNLEN, OFFSET, RMSELEN)
/usr/local/envs/py3env/lib/python3.5/site-packages/matplotlib/ UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
RMSE on 109 predictions (shaded area): 7.533706862258471
RMSE on 109 predictions (shaded area): 4.339749141322028
RMSE on 109 predictions (shaded area): 3.4786171332308053