Forecasting by LSTM net, 10.12.2019

for forecasting the next day’s closing price

https://stackabuse.com/time-series-analysis-with-lstm-using-pythons-keras-library/

import numpy as np  
import matplotlib.pyplot as plt  
import pandas as pd  
google_training_complete = pd.read_csv('D://oktatas/2019/aring/GOOG_train.csv') 
google_training_processed = google_training_complete.iloc[:, [5]].values #closing values
from sklearn.preprocessing import MinMaxScaler 
scaler = MinMaxScaler(feature_range = (0, 1))

google_training_scaled = scaler.fit_transform(google_training_processed)  

features_set = []  
labels = []  
nn=google_training_scaled.shape[0]
for i in range(60, nn):  
    features_set.append(google_training_scaled[i-60:i, 0])
    labels.append(google_training_scaled[i, 0])
features_set, labels = np.array(features_set), np.array(labels) 
features_set = np.reshape(features_set, (features_set.shape[0], features_set.shape[1], 1))  

import tensorflow as tf
import keras #first it has to be installed, together with tensorflow
#there were problems,  pip install -U numpy solved it

## Using TensorFlow backend.

from keras.models import Sequential  
from keras.layers import Dense  
from keras.layers import LSTM  
from keras.layers import Dropout 

model = Sequential() 
model.add(LSTM(units=50, return_sequences=True, input_shape=(features_set.shape[1], 1)))  
#an LSTM layer with 50 neurons

## --- Logging error ---
## Traceback (most recent call last):
##   File "C:\Users\zempleni\ANACON~1\lib\logging\__init__.py", line 985, in emit
##     stream.write(msg)
## ValueError: I/O operation on closed file
## Call stack:
##   File "<string>", line 1, in <module>
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\engine\sequential.py", line 165, in add
##     layer(x)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\layers\recurrent.py", line 532, in __call__
##     return super(RNN, self).__call__(inputs, **kwargs)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\engine\base_layer.py", line 431, in __call__
##     self.build(unpack_singleton(input_shapes))
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\layers\recurrent.py", line 493, in build
##     self.cell.build(step_input_shape)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\layers\recurrent.py", line 1868, in build
##     constraint=self.kernel_constraint)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
##     return func(*args, **kwargs)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\engine\base_layer.py", line 252, in add_weight
##     constraint=constraint)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 402, in variable
##     v = tf.Variable(value, dtype=tf.as_dtype(dtype), name=name)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\variables.py", line 213, in __call__
##     return cls._variable_v1_call(*args, **kwargs)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\variables.py", line 176, in _variable_v1_call
##     aggregation=aggregation)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\variables.py", line 155, in <lambda>
##     previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 2495, in default_variable_creator
##     expected_shape=expected_shape, import_scope=import_scope)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\variables.py", line 217, in __call__
##     return super(VariableMetaclass, cls).__call__(*args, **kwargs)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\variables.py", line 1395, in __init__
##     constraint=constraint)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\variables.py", line 1547, in _init_from_args
##     validate_shape=validate_shape).op
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\state_ops.py", line 223, in assign
##     validate_shape=validate_shape)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\gen_state_ops.py", line 67, in assign
##     use_locking=use_locking, name=name)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 784, in _apply_op_helper
##     with _MaybeColocateWith(must_colocate_inputs):
##   File "C:\Users\zempleni\ANACON~1\lib\contextlib.py", line 112, in __enter__
##     return next(self.gen)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 263, in _MaybeColocateWith
##     with ops.colocate_with(inputs[0]), _MaybeColocateWith(inputs[1:]):
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\util\deprecation.py", line 323, in new_func
##     instructions)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\platform\tf_logging.py", line 166, in warning
##     get_logger().warning(msg, *args, **kwargs)
## Message: 'From %s: %s (from %s) is deprecated and will be removed %s.\nInstructions for updating:\n%s'
## Arguments: ('C:\\Users\\zempleni\\ANACON~1\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263', 'colocate_with', 'tensorflow.python.framework.ops', 'in a future version', 'Colocations handled automatically by placer.')

model.add(Dropout(0.2))   #a dropout layer to avoid overfitting

## --- Logging error ---
## Traceback (most recent call last):
##   File "C:\Users\zempleni\ANACON~1\lib\logging\__init__.py", line 985, in emit
##     stream.write(msg)
## ValueError: I/O operation on closed file
## Call stack:
##   File "<string>", line 1, in <module>
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\engine\sequential.py", line 181, in add
##     output_tensor = layer(self.outputs[0])
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\engine\base_layer.py", line 457, in __call__
##     output = self.call(inputs, **kwargs)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\layers\core.py", line 126, in call
##     training=training)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3123, in in_train_phase
##     x = switch(training, x, alt)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3058, in switch
##     else_expression_fn)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
##     return func(*args, **kwargs)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 2097, in cond
##     orig_res_t, res_t = context_t.BuildCondBranch(true_fn)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 1941, in BuildCondBranch
##     original_result = fn()
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\layers\core.py", line 124, in dropped_inputs
##     seed=self.seed)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\keras\backend\tensorflow_backend.py", line 3445, in dropout
##     return tf.nn.dropout(x * 1., retain_prob, noise_shape, seed=seed)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\util\deprecation.py", line 506, in new_func
##     instructions)
##   File "C:\Users\zempleni\ANACON~1\lib\site-packages\tensorflow\python\platform\tf_logging.py", line 166, in warning
##     get_logger().warning(msg, *args, **kwargs)
## Message: 'From %s: calling %s (from %s) with %s is deprecated and will be removed %s.\nInstructions for updating:\n%s'
## Arguments: ('C:\\Users\\zempleni\\ANACON~1\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445', 'dropout', 'tensorflow.python.ops.nn_ops', 'keep_prob', 'in a future version', 'Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.')

model.add(LSTM(units=50, return_sequences=True))  
model.add(Dropout(0.2))

model.add(LSTM(units=50, return_sequences=True))  
model.add(Dropout(0.2))

model.add(LSTM(units=50))  
model.add(Dropout(0.2))  
model.add(Dense(units = 1))   #to predict a single value

model.compile(optimizer = 'adam', loss = 'mean_squared_error')  
model.fit(features_set, labels, epochs = 100, batch_size = 32)  

## Epoch 1/100
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## Epoch 2/100
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0023
## Epoch 3/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0022
## Epoch 4/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0017
## Epoch 5/100
## 
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## 3306/3306 [==============================] - 15s 5ms/step - loss: 0.0015
## Epoch 6/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0015
## Epoch 7/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0015
## Epoch 8/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0015
## Epoch 9/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0013
## Epoch 10/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0013
## Epoch 11/100
## 
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## 3306/3306 [==============================] - 17s 5ms/step - loss: 0.0015
## Epoch 12/100
## 
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## 3306/3306 [==============================] - 17s 5ms/step - loss: 0.0012
## Epoch 13/100
## 
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## 3306/3306 [==============================] - 17s 5ms/step - loss: 0.0013
## Epoch 14/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0012
## Epoch 15/100
## 
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## 3306/3306 [==============================] - 17s 5ms/step - loss: 0.0011
## Epoch 16/100
## 
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## 3306/3306 [==============================] - 17s 5ms/step - loss: 0.0011
## Epoch 17/100
## 
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## 3306/3306 [==============================] - 17s 5ms/step - loss: 9.7564e-04
## Epoch 18/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0011
## Epoch 19/100
## 
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## 3306/3306 [==============================] - 15s 5ms/step - loss: 9.4322e-04
## Epoch 20/100
## 
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## 3306/3306 [==============================] - 15s 5ms/step - loss: 9.2680e-04
## Epoch 21/100
## 
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## 3306/3306 [==============================] - 15s 5ms/step - loss: 9.5601e-04
## Epoch 22/100
## 
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## 3306/3306 [==============================] - 15s 5ms/step - loss: 8.8426e-04
## Epoch 23/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 0.0011
## Epoch 24/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 8.4032e-04
## Epoch 25/100
## 
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## 3296/3306 [============================>.] - ETA: 0s - loss: 8.3534e-04
## 3306/3306 [==============================] - 16s 5ms/step - loss: 8.3513e-04
## Epoch 26/100
## 
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## 3306/3306 [==============================] - 15s 5ms/step - loss: 7.8953e-04
## Epoch 27/100
## 
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## 3306/3306 [==============================] - 15s 5ms/step - loss: 7.8398e-04
## Epoch 28/100
## 
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## 3306/3306 [==============================] - 15s 5ms/step - loss: 7.8880e-04
## Epoch 29/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 7.5728e-04
## Epoch 30/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 7.4427e-04
## Epoch 31/100
## 
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## 3306/3306 [==============================] - 22s 7ms/step - loss: 7.5624e-04
## Epoch 32/100
## 
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## 3306/3306 [==============================] - 19s 6ms/step - loss: 8.3319e-04
## Epoch 33/100
## 
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## 3306/3306 [==============================] - 16s 5ms/step - loss: 7.8033e-04
## Epoch 34/100
## 
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## 3306/3306 [==============================] - 17s 5ms/step - loss: 7.0713e-04
## Epoch 35/100
## 
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## 3306/3306 [==============================] - 18s 5ms/step - loss: 6.5591e-04
## Epoch 36/100
## 
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## 3306/3306 [==============================] - 20s 6ms/step - loss: 6.8214e-04
## Epoch 37/100
## 
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## 3306/3306 [==============================] - 17s 5ms/step - loss: 7.3409e-04
## Epoch 38/100
## 
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