Error.py

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In [ ]:

​
In [1]:

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
​
dataset = np.loadtxt('normalized_ibm_prices.csv')
​
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
​
plt.plot(dataset)
plt.xlabel('time period')
plt.ylabel('normalized series value')
executed in 265ms, finished 21:27:20 2017-06-29
Out[1]:
<matplotlib.text.Text at 0x7f3cc0f842b0>

In [4]:

+idx
def window_transform_series(series,window_size,idx = 3):
    # containers for input/output pairs
    X = []
    y = []
​
    upper_range = len(series)-window_size
    for i in range(0,upper_range):               
        window_index = i+window_size
        X_temp = [series[j] for j in range(i,window_index) ]
        X.append(X_temp)        
        y.append( series[i+window_size:i+window_size+idx])                        
        
    # reshape each 
    X = np.asarray(X)
    dir((np.shape(X)[0:2]))    
    X.shape = (np.shape(X)[0:2])
    y = np.asarray(y)
    y.reshape(len(y),1)
    return X,y 
​
​
window_size = 7
X,y = window_transform_series(dataset,7)
print (X.shape)
print (y.shape)
print(X[0])
print(y[0])
executed in 42ms, finished 21:28:01 2017-06-29
(1756, 7)
(1756,)
[ 109.622681  110.152489  109.454445  109.874969  108.739616  109.420853
  107.806137]
[ 106.906273  108.470512  105.199074]
In [6]:

# split our dataset into training / testing sets
train_test_split = int(np.ceil(2*len(y)/float(3)))   # set the split point
​
# partition the training set
X_train = X[:train_test_split,:]
y_train = y[:train_test_split]
​
# keep the last chunk for testing
X_test = X[train_test_split:,:]
y_test = y[train_test_split:]
​
# NOTE: to use keras's RNN LSTM module our input must be reshaped to [samples, window size, stepsize] 
X_train = np.asarray(np.reshape(X_train, (X_train.shape[0], window_size, 1)))
X_test = np.asarray(np.reshape(X_test, (X_test.shape[0], window_size, 1)))
​
executed in 10ms, finished 21:28:35 2017-06-29
In [7]:

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.callbacks import EarlyStopping
import keras
​
# given - fix random seed - so we can all reproduce the same results on our default time series
np.random.seed(0)
​
​
# TODO: build an RNN to perform regression on our time series input/output data
​
model = Sequential()
model.add(LSTM(5, input_shape = (window_size,1)))
model.add(Dense(1, activation=None))
​
print(len(X_train))
print(len(y_train))
​
# build model using keras documentation recommended optimizer initialization
optimizer = keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
​
# compile the model
model.compile(loss='mean_squared_error', optimizer=optimizer)
​
model.fit(X_train, y_train, epochs=100, verbose=2)
executed in 7.78s, finished 21:28:48 2017-06-29
Using Theano backend.
WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10).  Please switch to the gpuarray backend. You can get more information about how to switch at this URL:
 https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29

Using gpu device 0: GeForce GTX 850M (CNMeM is enabled with initial size: 75.0% of memory, cuDNN 5110)
1171
1171
Epoch 1/100
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-7-5cf5b03019f2> in <module>()
     24 model.compile(loss='mean_squared_error', optimizer=optimizer)
     25 
---> 26 model.fit(X_train, y_train, epochs=100, verbose=2)

/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/keras/models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
    868                               class_weight=class_weight,
    869                               sample_weight=sample_weight,
--> 870                               initial_epoch=initial_epoch)
    871 
    872     def evaluate(self, x, y, batch_size=32, verbose=1,

/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
   1505                               val_f=val_f, val_ins=val_ins, shuffle=shuffle,
   1506                               callback_metrics=callback_metrics,
-> 1507                               initial_epoch=initial_epoch)
   1508 
   1509     def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):

/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
   1154                 batch_logs['size'] = len(batch_ids)
   1155                 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1156                 outs = f(ins_batch)
   1157                 if not isinstance(outs, list):
   1158                     outs = [outs]

/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/keras/backend/theano_backend.py in __call__(self, inputs)
   1194     def __call__(self, inputs):
   1195         assert isinstance(inputs, (list, tuple))
-> 1196         return self.function(*inputs)
   1197 
   1198 

/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/theano/compile/function_module.py in __call__(self, *args, **kwargs)
    793                         s.storage[0] = s.type.filter(
    794                             arg, strict=s.strict,
--> 795                             allow_downcast=s.allow_downcast)
    796 
    797                     except Exception as e:

/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/theano/tensor/type.py in filter(self, data, strict, allow_downcast)
    115             if allow_downcast:
    116                 # Convert to self.dtype, regardless of the type of data
--> 117                 data = theano._asarray(data, dtype=self.dtype)
    118                 # TODO: consider to pad shape with ones to make it consistent
    119                 # with self.broadcastable... like vector->row type thing

/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/theano/misc/safe_asarray.py in _asarray(a, dtype, order)
     32         dtype = theano.config.floatX
     33     dtype = np.dtype(dtype)  # Convert into dtype object.
---> 34     rval = np.asarray(a, dtype=dtype, order=order)
     35     # Note that dtype comparison must be done by comparing their `num`
     36     # attribute. One cannot assume that two identical data types are pointers

/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
    529 
    530     """
--> 531     return array(a, dtype, copy=False, order=order)
    532 
    533 

ValueError: Bad input argument to theano function with name "train_function" at index 1 (0-based).  
Backtrace when that variable is created:

  File "/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2683, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2787, in run_ast_nodes
    if self.run_code(code, result):
  File "/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2847, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-7-5cf5b03019f2>", line 24, in <module>
    model.compile(loss='mean_squared_error', optimizer=optimizer)
  File "/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/keras/models.py", line 788, in compile
    **kwargs)
  File "/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/keras/engine/training.py", line 890, in compile
    dtype=K.dtype(self.outputs[i]))
  File "/home/q/anaconda3/envs/p3/lib/python3.6/site-packages/keras/backend/theano_backend.py", line 221, in placeholder
    x = T.TensorType(dtype, broadcast)(name)
setting an array element with a sequence.