Recurrent postprocessing: v1

This file takes as input a sequence of data from the CNN, and attempts to refine that into a more accurate output command.

Some credit belongs to https://github.com/harvitronix/five-video-classification-methods/blob/master/models.py for providing inspiration.


In [1]:
#Create references to important directories we will use over and over
import os, sys
DATA_HOME_DIR = '/home/nathan/olin/spring2017/line-follower/line-follower/data'

In [2]:
#import modules
import numpy as np
from glob import glob
from PIL import Image
from tqdm import tqdm
import bcolz

from matplotlib import pyplot as plt
import seaborn as sns
%matplotlib inline

In [3]:
from keras.layers import *
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers.wrappers import TimeDistributed
from keras.metrics import categorical_crossentropy, categorical_accuracy


Using TensorFlow backend.

In [4]:
%cd $DATA_HOME_DIR

path = DATA_HOME_DIR
train_path1=path + '/sun_apr_16_office_full_line_1'
train_path2=path + '/qea_blob_1'
# valid_path1=path + '/sun_apr_16_office_full_line_2'
# valid_path2=path + '/qea_blob_2'
valid_path1=path + '/qea-square_3'#+ '/sun_apr_16_office_full_line_2'

# train_path=path + '/qea_blob_1'
# valid_path=path + '/qea_blob_2'


/home/nathan/olin/spring2017/line-follower/line-follower/data

Gather data


In [5]:
INPUT_LEN = 512 # The number of columns in the CSV
WINDOW_SIZE = 16

In [6]:
def load_array(fname):
    return bcolz.open(fname)[:]

def windows(X, Y, seq_len=10):
    assert len(X) == len(Y)
    
    result = []
    for index in range(X.shape[0] - seq_len):
        result.append([X[index:index+seq_len], Y[index+seq_len-1]])
    result = np.array(result)
#     np.random.shuffle(result)
    return np.array(list(result[:,0])), np.array(list(result[:,1]))

def get_data(paths):
    Y_return = []
    for path in paths:
        %cd $path
        Y_train = np.genfromtxt('cmd_vel.csv', delimiter=',')[:,1] # only use turning angle
        Y_train = np.concatenate((Y_train, Y_train*-1))
        
        Y_return.extend(Y_train)
        
    X_all = load_array(paths[-1]+'/X_train_features3.b')
    X_all = np.reshape(X_all, (len(X_all), INPUT_LEN))
        
    print (len(X_all), len(Y_return))
    
    X_windowed, Y_windowed = windows(X_all, Y_return, WINDOW_SIZE)

    return np.array(X_windowed), np.array(Y_windowed)

In [7]:
X_train, Y_train = get_data([train_path1, train_path2])
X_valid, Y_valid = get_data([valid_path1])


/home/nathan/olin/spring2017/line-follower/line-follower/data/sun_apr_16_office_full_line_1
/home/nathan/olin/spring2017/line-follower/line-follower/data/qea_blob_1
1312 1312
/home/nathan/olin/spring2017/line-follower/line-follower/data/qea-square_3
190 190

In [8]:
X_train.shape


Out[8]:
(1296, 16, 512)

Network


In [9]:
in_shape = (WINDOW_SIZE, INPUT_LEN)

In [10]:
def get_model():
    model = Sequential([
            LSTM(2048, return_sequences=False, input_shape=in_shape),
            Dropout(0.5),
#             Flatten(input_shape=in_shape),
            Dense(512, activation='relu'),
#             Dense(512, activation='relu'),
#             Dense(512, activation='relu'),
            Dropout(0.5),
            Dense(1)
        ])
    model.compile(loss='mean_absolute_error', optimizer='adam')
    
    return model
    
model = get_model()
model.summary()


____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
lstm_1 (LSTM)                    (None, 2048)          20979712    lstm_input_1[0][0]               
____________________________________________________________________________________________________
dropout_1 (Dropout)              (None, 2048)          0           lstm_1[0][0]                     
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 512)           1049088     dropout_1[0][0]                  
____________________________________________________________________________________________________
dropout_2 (Dropout)              (None, 512)           0           dense_1[0][0]                    
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 1)             513         dropout_2[0][0]                  
====================================================================================================
Total params: 22,029,313
Trainable params: 22,029,313
Non-trainable params: 0
____________________________________________________________________________________________________

Train the model


In [12]:
%cd $DATA_HOME_DIR
model.load_weights('LSTM_postprocessor_v1.h5')


/home/nathan/olin/spring2017/line-follower/line-follower/data

In [32]:
history = model.fit(X_train, Y_train,#X_train[:,-1],
                    batch_size = 96,
                    nb_epoch=150,
                    validation_data=(X_valid, Y_valid),
                    verbose=True)


Train on 1296 samples, validate on 578 samples
Epoch 1/150
1296/1296 [==============================] - 1s - loss: 0.0138 - val_loss: 0.0398
Epoch 2/150
1296/1296 [==============================] - 1s - loss: 0.0138 - val_loss: 0.0398
Epoch 3/150
1296/1296 [==============================] - 1s - loss: 0.0138 - val_loss: 0.0391
Epoch 4/150
1296/1296 [==============================] - 1s - loss: 0.0134 - val_loss: 0.0400
Epoch 5/150
1296/1296 [==============================] - 1s - loss: 0.0136 - val_loss: 0.0393
Epoch 6/150
1296/1296 [==============================] - 1s - loss: 0.0127 - val_loss: 0.0390
Epoch 7/150
1296/1296 [==============================] - 1s - loss: 0.0122 - val_loss: 0.0393
Epoch 8/150
1296/1296 [==============================] - 1s - loss: 0.0123 - val_loss: 0.0391
Epoch 9/150
1296/1296 [==============================] - 1s - loss: 0.0122 - val_loss: 0.0385
Epoch 10/150
1296/1296 [==============================] - 1s - loss: 0.0117 - val_loss: 0.0393
Epoch 11/150
1296/1296 [==============================] - 1s - loss: 0.0115 - val_loss: 0.0387
Epoch 12/150
1296/1296 [==============================] - 1s - loss: 0.0120 - val_loss: 0.0384
Epoch 13/150
1296/1296 [==============================] - 1s - loss: 0.0112 - val_loss: 0.0394
Epoch 14/150
1296/1296 [==============================] - 1s - loss: 0.0119 - val_loss: 0.0388
Epoch 15/150
1296/1296 [==============================] - 1s - loss: 0.0121 - val_loss: 0.0393
Epoch 16/150
1296/1296 [==============================] - 1s - loss: 0.0107 - val_loss: 0.0388
Epoch 17/150
1296/1296 [==============================] - 1s - loss: 0.0109 - val_loss: 0.0388
Epoch 18/150
1296/1296 [==============================] - 1s - loss: 0.0110 - val_loss: 0.0386
Epoch 19/150
1296/1296 [==============================] - 1s - loss: 0.0106 - val_loss: 0.0387
Epoch 20/150
1296/1296 [==============================] - 1s - loss: 0.0105 - val_loss: 0.0382
Epoch 21/150
1296/1296 [==============================] - 1s - loss: 0.0102 - val_loss: 0.0386
Epoch 22/150
1296/1296 [==============================] - 1s - loss: 0.0103 - val_loss: 0.0398
Epoch 23/150
1296/1296 [==============================] - 1s - loss: 0.0106 - val_loss: 0.0396
Epoch 24/150
1296/1296 [==============================] - 1s - loss: 0.0101 - val_loss: 0.0390
Epoch 25/150
1296/1296 [==============================] - 1s - loss: 0.0102 - val_loss: 0.0395
Epoch 26/150
1296/1296 [==============================] - 1s - loss: 0.0096 - val_loss: 0.0384
Epoch 27/150
1296/1296 [==============================] - 1s - loss: 0.0100 - val_loss: 0.0387
Epoch 28/150
1296/1296 [==============================] - 1s - loss: 0.0097 - val_loss: 0.0387
Epoch 29/150
1296/1296 [==============================] - 1s - loss: 0.0099 - val_loss: 0.0389
Epoch 30/150
1296/1296 [==============================] - 1s - loss: 0.0102 - val_loss: 0.0392
Epoch 31/150
1296/1296 [==============================] - 1s - loss: 0.0101 - val_loss: 0.0396
Epoch 32/150
1296/1296 [==============================] - 1s - loss: 0.0096 - val_loss: 0.0390
Epoch 33/150
1296/1296 [==============================] - 1s - loss: 0.0093 - val_loss: 0.0396
Epoch 34/150
1296/1296 [==============================] - 1s - loss: 0.0098 - val_loss: 0.0386
Epoch 35/150
1296/1296 [==============================] - 1s - loss: 0.0095 - val_loss: 0.0387
Epoch 36/150
1296/1296 [==============================] - 1s - loss: 0.0095 - val_loss: 0.0387
Epoch 37/150
1296/1296 [==============================] - 1s - loss: 0.0102 - val_loss: 0.0392
Epoch 38/150
1296/1296 [==============================] - 1s - loss: 0.0091 - val_loss: 0.0388
Epoch 39/150
1296/1296 [==============================] - 1s - loss: 0.0096 - val_loss: 0.0401
Epoch 40/150
1296/1296 [==============================] - 1s - loss: 0.0090 - val_loss: 0.0385
Epoch 41/150
1296/1296 [==============================] - 1s - loss: 0.0099 - val_loss: 0.0391
Epoch 42/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0398
Epoch 43/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0388
Epoch 44/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0393
Epoch 45/150
1296/1296 [==============================] - 1s - loss: 0.0094 - val_loss: 0.0393
Epoch 46/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0387
Epoch 47/150
1296/1296 [==============================] - 1s - loss: 0.0094 - val_loss: 0.0395
Epoch 48/150
1296/1296 [==============================] - 1s - loss: 0.0092 - val_loss: 0.0389
Epoch 49/150
1296/1296 [==============================] - 1s - loss: 0.0095 - val_loss: 0.0385
Epoch 50/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0388
Epoch 51/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0388
Epoch 52/150
1296/1296 [==============================] - 1s - loss: 0.0092 - val_loss: 0.0393
Epoch 53/150
1296/1296 [==============================] - 1s - loss: 0.0091 - val_loss: 0.0391
Epoch 54/150
1296/1296 [==============================] - 1s - loss: 0.0093 - val_loss: 0.0397
Epoch 55/150
1296/1296 [==============================] - 1s - loss: 0.0090 - val_loss: 0.0386
Epoch 56/150
1296/1296 [==============================] - 1s - loss: 0.0089 - val_loss: 0.0392
Epoch 57/150
1296/1296 [==============================] - 1s - loss: 0.0091 - val_loss: 0.0394
Epoch 58/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0390
Epoch 59/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0386
Epoch 60/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0401
Epoch 61/150
1296/1296 [==============================] - 1s - loss: 0.0089 - val_loss: 0.0389
Epoch 62/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0391
Epoch 63/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0399
Epoch 64/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0385
Epoch 65/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0395
Epoch 66/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0388
Epoch 67/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0394
Epoch 68/150
1296/1296 [==============================] - 1s - loss: 0.0093 - val_loss: 0.0396
Epoch 69/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0386
Epoch 70/150
1296/1296 [==============================] - 1s - loss: 0.0090 - val_loss: 0.0391
Epoch 71/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0389
Epoch 72/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0395
Epoch 73/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0392
Epoch 74/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0385
Epoch 75/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0387
Epoch 76/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0391
Epoch 77/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0398
Epoch 78/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0392
Epoch 79/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0387
Epoch 80/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0388
Epoch 81/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0390
Epoch 82/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0394
Epoch 83/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0392
Epoch 84/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0394
Epoch 85/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0387
Epoch 86/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0392
Epoch 87/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0387
Epoch 88/150
1296/1296 [==============================] - 1s - loss: 0.0090 - val_loss: 0.0390
Epoch 89/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0388
Epoch 90/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0388
Epoch 91/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0397
Epoch 92/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0394
Epoch 93/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0392
Epoch 94/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0393
Epoch 95/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0401
Epoch 96/150
1296/1296 [==============================] - 1s - loss: 0.0092 - val_loss: 0.0392
Epoch 97/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0400
Epoch 98/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0388
Epoch 99/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0394
Epoch 100/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0389
Epoch 101/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0395
Epoch 102/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0389
Epoch 103/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0394
Epoch 104/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0395
Epoch 105/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0395
Epoch 106/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0390
Epoch 107/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0397
Epoch 108/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0400
Epoch 109/150
1296/1296 [==============================] - 1s - loss: 0.0077 - val_loss: 0.0391
Epoch 110/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0401
Epoch 111/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0388
Epoch 112/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0392
Epoch 113/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0391
Epoch 114/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0390
Epoch 115/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0395
Epoch 116/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0395
Epoch 117/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0394
Epoch 118/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0388
Epoch 119/150
1296/1296 [==============================] - 1s - loss: 0.0089 - val_loss: 0.0398
Epoch 120/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0390
Epoch 121/150
1296/1296 [==============================] - 1s - loss: 0.0097 - val_loss: 0.0397
Epoch 122/150
1296/1296 [==============================] - 1s - loss: 0.0091 - val_loss: 0.0391
Epoch 123/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0387
Epoch 124/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0404
Epoch 125/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0391
Epoch 126/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0391
Epoch 127/150
1296/1296 [==============================] - 1s - loss: 0.0076 - val_loss: 0.0391
Epoch 128/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0390
Epoch 129/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0390
Epoch 130/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0393
Epoch 131/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0394
Epoch 132/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0391
Epoch 133/150
1296/1296 [==============================] - 1s - loss: 0.0075 - val_loss: 0.0391
Epoch 134/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0389
Epoch 135/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0401
Epoch 136/150
1296/1296 [==============================] - 1s - loss: 0.0076 - val_loss: 0.0400
Epoch 137/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0389
Epoch 138/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0396
Epoch 139/150
1296/1296 [==============================] - 1s - loss: 0.0077 - val_loss: 0.0389
Epoch 140/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0386
Epoch 141/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0393
Epoch 142/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0398
Epoch 143/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0392
Epoch 144/150
1296/1296 [==============================] - 1s - loss: 0.0076 - val_loss: 0.0391
Epoch 145/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0389
Epoch 146/150
1296/1296 [==============================] - 1s - loss: 0.0078 - val_loss: 0.0389
Epoch 147/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0393
Epoch 148/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0394
Epoch 149/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0391
Epoch 150/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0391

In [13]:
conv_predictions = X_train[:,-1]
recurrent_predictions = model.predict(X_train)
ground_truth = Y_train
for x,y,z in zip(conv_predictions[:,0], ground_truth, recurrent_predictions[:,0]):
    print ("{:07f}\t{:07f}\t{:07f}\t".format(x,y,z))


0.000000	0.009191	-0.001004	
0.000000	-0.000000	-0.000995	
0.000000	-0.000000	-0.000988	
0.000000	-0.000000	-0.000984	
0.000000	-0.000000	-0.000985	
0.000000	-0.000000	-0.000986	
0.000000	-0.084054	-0.092041	
0.000000	-0.000000	-0.001812	
0.000000	-0.000000	-0.001331	
0.000000	-0.000000	-0.001100	
0.000000	-0.000000	-0.001096	
0.000000	-0.000000	-0.001089	
0.000000	-0.000000	-0.001059	
0.000000	0.064105	0.063707	
0.000000	0.036648	0.032237	
0.000000	0.006695	-0.000578	
0.000000	0.046632	0.044549	
0.000000	0.076585	0.073129	
0.000000	-0.000000	-0.000434	
0.000000	-0.000000	-0.000453	
0.000000	0.031656	0.035124	
0.000000	0.061609	0.038705	
0.000000	0.096564	0.094046	
0.000000	0.049128	0.046650	
0.000000	0.026663	0.027809	
0.000000	0.153974	0.090551	
0.000000	-0.000000	-0.000555	
0.000000	0.069097	0.068128	
0.000000	0.079081	0.081587	
0.000000	0.054120	0.055685	
0.000000	0.049128	0.047165	
0.000000	0.049128	0.046470	
0.000000	0.079081	0.078815	
0.000000	0.081577	0.080389	
0.000000	0.026663	0.030959	
0.000000	0.036648	0.036661	
0.000000	0.106548	0.104779	
0.000000	0.106548	0.109401	
0.000000	0.151478	0.139595	
0.000000	-0.000000	0.000471	
0.000000	0.079081	0.071040	
0.000000	-0.000000	0.000411	
0.000000	0.009191	-0.000061	
0.000000	-0.000000	-0.000369	
0.000000	-0.000000	-0.000689	
0.000000	-0.000000	-0.000756	
0.000000	-0.000000	-0.000786	
0.000000	0.064105	0.056766	
0.000000	-0.000000	-0.000879	
0.000000	-0.000000	-0.000727	
0.000000	-0.000000	-0.000717	
0.000000	-0.000000	-0.000807	
0.000000	-0.000000	-0.000902	
0.000000	-0.000000	-0.000918	
0.000000	-0.000000	-0.000916	
0.000000	-0.124001	-0.123337	
0.000000	-0.000000	-0.002002	
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0.000000	0.000000	0.000571	
0.000000	0.000000	0.000091	
0.000000	0.044117	0.047727	
0.000000	0.044117	0.046691	
0.000000	0.079062	0.083651	
0.000000	0.114017	0.111260	
0.000000	0.000000	0.000003	
0.000000	0.076566	0.078584	
0.000000	0.266288	0.253005	
0.000000	0.300000	0.300095	
0.000000	0.300000	0.286962	
0.000000	0.158947	0.147040	
0.000000	0.069078	0.071832	
0.000000	0.300000	0.249850	
0.000000	0.258800	0.250639	
0.000000	0.138978	0.156292	
0.000000	0.036628	0.044762	
0.000000	0.196388	0.174634	
0.000000	0.286257	0.281708	
0.000000	0.091552	0.100462	
0.000000	0.111521	0.114529	
0.000000	0.163939	0.160138	
0.000000	0.101537	0.099793	
0.000000	0.213870	0.207572	
0.000000	0.099040	0.100953	
0.000000	0.009171	0.009836	
0.000000	0.186404	0.164696	
0.000000	0.136482	0.137895	
0.000000	0.000000	0.000877	
0.000000	0.000000	0.000563	
0.000000	0.000000	0.000276	
0.000000	0.000000	-0.000207	
0.000000	0.031636	0.023980	
0.000000	0.101537	0.103077	
0.000000	0.089046	0.092184	
0.000000	0.071574	0.066782	
0.000000	0.054101	0.048541	
0.000000	0.054101	0.052658	
0.000000	0.116513	0.108557	
0.000000	0.064085	0.067888	
0.000000	0.133986	0.132496	
0.000000	0.089046	0.091570	
0.000000	0.059093	0.059624	
0.000000	0.151458	0.169919	
0.000000	0.121505	0.110003	
0.000000	0.101537	0.102289	
0.000000	0.101537	0.105484	
0.000000	0.101537	0.100459	
0.000000	0.089046	0.096442	
0.000000	0.086550	0.087983	
0.000000	0.086550	0.091806	
0.000000	0.086550	0.078520	
0.000000	0.051605	0.052839	
0.000000	0.000000	0.000351	
0.000000	0.000000	-0.000060	
0.000000	0.000000	-0.000369	
0.000000	0.009171	-0.000282	
0.000000	0.064085	0.061278	
0.000000	0.064085	0.061407	
0.000000	0.074070	0.074023	
0.000000	0.056597	0.053217	
0.000000	0.056597	0.061243	
0.000000	0.054101	0.054105	
0.000000	0.079062	0.077895	
0.000000	0.000000	0.000387	
0.000000	0.106529	0.103914	
0.000000	0.136482	0.143282	
0.000000	0.099040	0.094190	
0.000000	0.076566	0.078268	
0.000000	0.049109	0.048224	
0.000000	0.300000	0.278158	
0.000000	0.208878	0.200532	
0.000000	0.218863	0.210273	
0.000000	0.000000	0.004221	
0.000000	0.000000	0.000998	
0.000000	-0.178935	-0.161722	
0.000000	0.034132	0.039643	
0.000000	0.056597	0.054805	
0.000000	0.000000	-0.000973	
0.000000	0.000000	-0.000725	
0.000000	0.000000	-0.000765	
0.000000	0.133986	0.127147	
0.000000	0.131490	0.132855	
0.000000	0.000000	-0.000460	
0.000000	0.054101	0.049390	
0.000000	0.059093	0.061110	
0.000000	0.000000	-0.000138	
0.000000	0.056597	0.069793	
0.000000	0.056597	0.058587	
0.000000	0.056597	0.052944	
0.000000	0.069078	0.070125	
0.000000	0.069078	0.071082	
0.000000	0.041621	0.041439	
0.000000	0.041621	0.043556	
0.000000	0.041621	0.040651	
0.000000	0.041621	0.043514	
0.000000	0.041621	0.042780	
0.000000	0.126497	0.124421	
0.000000	0.133986	0.132113	
0.000000	0.133986	0.123277	
0.000000	0.016660	0.014426	
0.000000	0.064085	0.061289	
0.000000	0.000000	0.000400	
0.000000	0.000000	0.000099	
0.000000	0.000000	-0.000333	
0.000000	0.019156	0.006005	
0.000000	0.049109	0.050703	
0.000000	0.061589	0.059157	
0.000000	0.064085	0.061571	
0.000000	0.066582	0.066227	
0.000000	0.066582	0.063195	
0.000000	0.069078	0.061473	
0.000000	0.019156	0.023534	
0.000000	0.128994	0.130996	
0.000000	0.000000	0.000534	
0.000000	0.096544	0.090048	
0.000000	0.099040	0.104891	
0.000000	0.094048	0.094627	
0.000000	0.000000	-0.000071	
0.000000	0.000000	-0.000121	
0.000000	0.014164	0.006359	
0.000000	0.101537	0.095603	
0.000000	0.133986	0.131191	
0.000000	0.218863	0.202433	
0.000000	0.000000	0.000503	
0.000000	0.000000	0.004340	
0.000000	0.049109	0.052624	
0.000000	0.276273	0.203362	
0.000000	0.288753	0.319283	
0.000000	0.246320	0.212487	
0.000000	0.131490	0.115534	
0.000000	0.201390	0.192231	
0.000000	0.121505	0.115552	
0.000000	0.300000	0.266273	
0.000000	0.233839	0.196809	
0.000000	0.011668	0.006703	
0.000000	0.213870	0.215814	
0.000000	0.298738	0.237943	
0.000000	0.024148	0.026641	
0.000000	0.046613	0.051311	
0.000000	0.151458	0.153719	
0.000000	0.178915	0.170903	
0.000000	0.173923	0.177773	
0.000000	0.173923	0.175033	
0.000000	0.109025	0.112041	
0.000000	0.000000	0.001005	
0.000000	0.091552	0.104169	
0.000000	0.094048	0.111637	
0.000000	0.000000	0.000975	
0.000000	0.056597	0.059448	
0.000000	0.000000	-0.000760	
0.000000	0.000000	-0.000400	
0.000000	0.014164	0.002899	
0.000000	0.049109	0.045917	
0.000000	0.099040	0.095448	
0.000000	0.099040	0.097761	
0.000000	0.024148	0.013367	
0.000000	0.059093	0.058204	
0.000000	0.104033	0.104789	
0.000000	0.109025	0.106822	
0.000000	0.109025	0.100059	
0.000000	0.111521	0.105975	
0.000000	0.133986	0.117513	
0.000000	0.133986	0.123373	
0.000000	0.084054	0.092563	
0.000000	0.126497	0.117669	
0.000000	0.131490	0.126309	
0.000000	0.039125	0.038842	
0.000000	0.086550	0.087377	
0.000000	0.066582	0.067449	
0.000000	0.066582	0.068219	
0.000000	0.089046	0.082055	
0.000000	0.000000	0.000816	
0.000000	-0.178935	-0.181573	
0.000000	0.049109	0.048602	
0.000000	0.079062	0.070765	
0.000000	0.101537	0.098706	
0.000000	0.000000	-0.000885	
0.000000	0.168931	0.167903	
0.000000	0.181411	0.171587	
0.000000	0.000000	0.001530	
0.000000	0.000000	0.000400	
0.000000	0.049109	0.050862	
0.000000	0.066582	0.067115	
0.000000	0.111521	0.122457	
0.000000	0.081558	0.085190	
0.000000	0.054101	0.052480	
0.000000	0.000000	0.004613	
0.000000	0.084054	0.084923	
0.000000	0.300000	0.288150	
0.000000	0.300000	0.289465	
0.000000	0.211374	0.218366	
0.000000	0.000000	0.001772	
0.000000	0.000000	0.000922	
0.000000	0.000000	0.000967	
0.000000	-0.300000	-0.280619	
0.000000	0.000000	-0.001225	
0.000000	0.009171	0.003127	
0.000000	0.101537	0.098198	
0.000000	0.099040	0.096768	
0.000000	0.094048	0.092971	
0.000000	0.079062	0.072339	
0.000000	0.051605	0.049462	
0.000000	0.000000	-0.000570	
0.000000	0.041621	0.043765	
0.000000	0.121505	0.108269	
0.000000	0.000000	0.000718	
0.000000	0.071574	0.074669	
0.000000	0.076566	0.079411	
0.000000	0.016660	0.012570	
0.000000	0.000000	0.000413	
0.000000	0.011668	0.001752	
0.000000	0.081558	0.074863	
0.000000	0.148962	0.129719	
0.000000	0.136482	0.138915	
0.000000	0.000000	0.000125	
0.000000	0.000000	0.000328	
0.000000	0.039125	0.033526	
0.000000	0.138978	0.127305	
0.000000	0.203886	0.181033	
0.000000	0.019156	0.021872	
0.000000	0.046613	0.045938	
0.000000	0.014164	0.014766	
0.000000	0.076566	0.071975	
0.000000	0.021652	0.021020	
0.000000	0.016660	0.017505	
0.000000	0.039125	0.040500	
0.000000	0.044117	0.037389	
0.000000	0.046613	0.042194	
0.000000	0.046613	0.043910	
0.000000	0.046613	0.043009	
0.000000	0.046613	0.042813	
0.000000	0.046613	0.050506	
0.000000	0.046613	0.045734	
0.000000	0.111521	0.108484	
0.000000	0.114017	0.112224	
0.000000	0.044117	0.043642	
0.000000	0.021652	0.016744	
0.000000	0.016660	0.017434	
0.000000	0.094048	0.088181	
0.000000	0.066582	0.066386	
0.000000	0.091552	0.089768	
0.000000	0.121505	0.111668	
0.000000	0.000000	0.006490	
0.000000	0.079062	0.094120	
0.000000	0.218863	0.157038	
0.000000	0.266288	0.256272	
0.000000	0.218863	0.212746	
0.000000	0.151458	0.151240	
0.000000	0.151458	0.145537	
0.000000	0.151458	0.150734	
0.000000	0.151458	0.123677	
0.000000	0.268784	0.237229	
0.000000	0.258800	0.228667	
0.000000	0.094048	0.096077	
0.000000	0.096544	0.092197	
0.000000	0.143970	0.149072	
0.000000	0.156451	0.160855	
0.000000	0.131490	0.129850	
0.000000	0.011668	0.006396	
0.000000	0.183908	0.180333	
0.000000	0.256304	0.242242	
0.000000	0.059093	0.068539	
0.000000	0.119009	0.123005	
0.000000	0.041621	0.041553	
0.000000	-0.116532	-0.121414	
0.000000	0.041621	0.041152	
0.000000	0.076566	0.084520	
0.000000	0.119009	0.111302	
0.000000	0.131490	0.124971	
0.000000	0.054101	0.051346	
0.000000	0.000000	0.000055	
0.000000	0.000000	-0.000107	
0.000000	0.021652	0.016663	
0.000000	0.056597	0.059296	
0.000000	0.064085	0.068848	
0.000000	0.086550	0.093655	
0.000000	0.101537	0.096815	
0.000000	0.131490	0.126745	
0.000000	0.158947	0.154455	
0.000000	0.158947	0.157132	
0.000000	0.056597	0.056364	
0.000000	0.114017	0.114350	
0.000000	0.114017	0.112726	
0.000000	0.114017	0.111345	
0.000000	0.114017	0.104528	
0.000000	0.114017	0.111591	
0.000000	0.114017	0.110559	
0.000000	0.081558	0.078485	
0.000000	0.064085	0.063452	
0.000000	0.000000	0.000728	
0.000000	0.000000	0.000235	
0.000000	0.000000	-0.000202	
0.000000	0.000000	-0.000424	
0.000000	0.000000	-0.000556	
0.000000	0.000000	-0.000669	
0.000000	0.074070	0.065950	
0.000000	0.124001	0.123965	
0.000000	0.163939	0.174008	
0.000000	0.000000	0.000637	
0.000000	0.000000	0.000116	
0.000000	0.049109	0.046857	
0.000000	0.059093	0.058893	
0.000000	0.206382	0.203215	
0.000000	0.156451	0.149862	
0.000000	0.000000	0.000342	
0.000000	0.000000	0.000107	
0.000000	0.121505	0.109923	
0.000000	0.300000	0.281857	
0.000000	0.300000	0.298539	
0.000000	0.268784	0.246460	
0.000000	-0.300000	-0.304607	
0.000000	0.000000	-0.000989	
0.000000	0.000000	-0.000552	
0.000000	0.000000	-0.000702	
0.000000	0.046613	0.046375	
0.000000	-0.084074	-0.093651	
0.000000	0.000000	-0.001092	
0.000000	0.000000	-0.000853	
0.000000	0.089046	0.091060	
0.000000	0.153954	0.142706	
0.000000	0.104033	0.094599	
0.000000	0.000000	-0.000603	
0.000000	0.000000	0.000235	
0.000000	0.148962	0.142012	
0.000000	0.054101	0.054444	

In [14]:
conv_predictions = X_valid[:,-1]
recurrent_predictions = model.predict(X_valid)
ground_truth = Y_valid
for x,y,z in zip(conv_predictions[:,0], ground_truth, recurrent_predictions[:,0]):
    print ("{:07f}\t{:07f}\t{:07f}\t".format(x,y,z))


0.000000	0.193911	0.081758	
0.000000	0.173942	0.076677	
0.000000	0.119028	0.014650	
0.000000	0.084074	-0.000172	
0.000000	0.014183	-0.004128	
0.000000	-0.000000	-0.088243	
0.000000	0.029159	-0.082580	
0.000000	0.001702	-0.083135	
0.000000	-0.000000	-0.002743	
0.000000	-0.000000	-0.002158	
0.000000	-0.000000	-0.080231	
0.000000	-0.000000	0.173954	
0.000000	-0.000000	0.167893	
0.000000	-0.000000	-0.098979	
0.000000	0.046632	-0.107860	
0.000000	0.300000	-0.001017	
0.000000	0.300000	-0.036726	
0.000000	0.300000	-0.039343	
0.000000	0.300000	-0.060210	
0.000000	0.300000	-0.001295	
0.000000	0.300000	0.066367	
0.000000	-0.000000	-0.001154	
0.000000	-0.000000	-0.002765	
0.000000	-0.099040	-0.063886	
0.000000	-0.044117	-0.055263	
0.000000	-0.000000	-0.001552	
0.000000	-0.000000	-0.001260	
0.000000	-0.000000	-0.001398	
0.000000	-0.000000	0.003892	
0.000000	-0.000000	0.081466	
0.000000	-0.000000	0.000975	
0.000000	0.104052	0.007476	
0.000000	0.096564	-0.082542	
0.000000	0.034152	-0.004605	
0.000000	0.106548	-0.036792	
0.000000	0.178935	-0.019905	
0.000000	0.166454	-0.001212	
0.000000	0.129013	-0.001005	
0.000000	0.129013	-0.001017	
0.000000	0.126517	-0.000761	
0.000000	0.126517	-0.000406	
0.000000	0.039144	0.043902	
0.000000	0.116532	0.084014	
0.000000	0.136501	0.079460	
0.000000	0.021671	0.037174	
0.000000	-0.000000	-0.000037	
0.000000	0.049128	-0.000468	
0.000000	-0.000000	-0.000532	
0.000000	-0.000000	0.004080	
0.000000	-0.000000	0.077464	
0.000000	-0.000000	-0.016794	
0.000000	0.064105	-0.009701	
0.000000	0.300000	-0.039424	
0.000000	0.300000	-0.064249	
0.000000	0.300000	-0.009397	
0.000000	0.300000	0.117577	
0.000000	0.064105	0.059870	
0.000000	0.041640	-0.000762	
0.000000	0.081577	-0.000706	
0.000000	0.148982	0.039586	
0.000000	-0.031636	-0.124632	
0.000000	-0.046613	-0.121112	
0.000000	-0.086550	-0.130548	
0.000000	-0.000000	-0.128613	
0.000000	-0.000000	-0.091758	
0.000000	-0.000000	0.019543	
0.000000	-0.000000	-0.131833	
0.000000	-0.000000	-0.009065	
0.000000	-0.000000	-0.008933	
0.000000	0.009191	0.060200	
0.000000	0.163958	0.061502	
0.000000	0.213890	0.040738	
0.000000	0.213890	0.180248	
0.000000	0.213890	-0.016544	
0.000000	0.216386	0.088849	
0.000000	0.263811	0.091260	
0.000000	0.138997	-0.001215	
0.000000	-0.000000	-0.000188	
0.000000	-0.000000	-0.049172	
0.000000	-0.000000	-0.003024	
0.000000	0.000000	-0.000894	
0.000000	0.000000	0.019083	
0.000000	0.000000	0.020166	
0.000000	0.000000	0.013614	
0.000000	0.000000	-0.000610	
0.000000	0.021652	0.055931	
0.000000	0.031636	0.021010	
0.000000	0.000000	-0.000596	
0.000000	0.000000	0.004136	
0.000000	0.000000	-0.119760	
0.000000	-0.049128	0.032473	
0.000000	-0.151478	0.274862	
0.000000	-0.178935	0.118322	
0.000000	-0.233858	-0.131727	
0.000000	-0.261315	-0.049573	
0.000000	-0.193911	-0.070714	
0.000000	-0.173942	-0.100913	
0.000000	-0.119028	-0.129752	
0.000000	-0.084074	-0.003298	
0.000000	-0.014183	-0.002384	
0.000000	0.000000	0.093493	
0.000000	-0.029159	0.001017	
0.000000	-0.001702	0.001084	
0.000000	0.000000	0.071739	
0.000000	0.000000	0.073913	
0.000000	0.000000	0.001126	
0.000000	0.000000	-0.008329	
0.000000	0.000000	0.024079	
0.000000	0.000000	0.014059	
0.000000	-0.046632	0.096195	
0.000000	-0.300000	0.007475	
0.000000	-0.300000	0.051323	
0.000000	-0.300000	0.051255	
0.000000	-0.300000	-0.011327	
0.000000	-0.300000	0.001796	
0.000000	-0.300000	-0.083289	
0.000000	0.000000	-0.000991	
0.000000	0.000000	-0.000693	
0.000000	0.099040	-0.000122	
0.000000	0.044117	-0.000881	
0.000000	0.000000	-0.000922	
0.000000	0.000000	-0.000213	
0.000000	0.000000	-0.001020	
0.000000	0.000000	-0.001032	
0.000000	0.000000	-0.044364	
0.000000	0.000000	-0.082304	
0.000000	-0.104052	-0.018657	
0.000000	-0.096564	-0.001497	
0.000000	-0.034152	-0.001223	
0.000000	-0.106548	0.000475	
0.000000	-0.178935	-0.001030	
0.000000	-0.166454	-0.001029	
0.000000	-0.129013	-0.001005	
0.000000	-0.129013	-0.001050	
0.000000	-0.126517	-0.002913	
0.000000	-0.126517	-0.077884	
0.000000	-0.039144	-0.046764	
0.000000	-0.116532	-0.001662	
0.000000	-0.136501	-0.090013	
0.000000	-0.021671	-0.020758	
0.000000	0.000000	-0.002006	
0.000000	-0.049128	-0.001395	
0.000000	0.000000	-0.001755	
0.000000	0.000000	-0.021354	
0.000000	0.000000	-0.117937	
0.000000	0.000000	-0.002591	
0.000000	-0.064105	-0.056122	
0.000000	-0.300000	-0.030594	
0.000000	-0.300000	-0.002041	
0.000000	-0.300000	-0.000944	
0.000000	-0.300000	-0.086194	
0.000000	-0.064105	-0.114011	
0.000000	-0.041640	-0.012291	
0.000000	-0.081577	-0.002990	
0.000000	-0.148982	0.036817	
0.000000	0.031636	0.016725	
0.000000	0.046613	0.093150	
0.000000	0.086550	0.092362	
0.000000	0.000000	0.093414	
0.000000	0.000000	0.007207	
0.000000	0.000000	-0.000240	
0.000000	0.000000	0.006954	
0.000000	0.000000	0.057264	
0.000000	0.000000	0.011452	
0.000000	-0.009191	-0.136083	
0.000000	-0.163958	0.017599	
0.000000	-0.213890	0.127445	
0.000000	-0.213890	-0.163605	
0.000000	-0.213890	0.002909	
0.000000	-0.216386	-0.047614	
0.000000	-0.263811	-0.049916	
0.000000	-0.138997	-0.139770	
0.000000	0.000000	-0.001853	
0.000000	0.000000	0.082127	

In [ ]:
plt.plot(ground_truth[:100])

Analyze training


In [ ]:
sns.tsplot(history.history['val_loss'])

In [ ]:
for x,y in zip(X_valid, Y_valid):
    print ("{}\t{}".format(np.reshape(x,(10,)),y))

In [ ]: