El presente diplomado hace parte del centro de Big Data de la facultad de ciencias exactas y naturales (FCEN) de la Universidad de Antioquia.
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%tensorflow_version 2.x
TensorFlow is already loaded. Please restart the runtime to change versions.
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from tensorflow import keras
The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.
We recommend you upgrade now
or ensure your notebook will continue to use TensorFlow 1.x via the %tensorflow_version 1.x magic:
.
El análisis de sentimientos es un tipo de problema de procesamiento del lenguaje natural que determina el sentimiento o la emoción relacionada con un texto. Por ejemplo, se podría construir un algoritmo para clasificar si la reseña de un producto es positiva, neutral o negativa. El procesamiento del lenguaje natural (NLP - Natural Language Processing) es un campo de la inteligencia artificial que implica que las computadoras entiendan y procesen el lenguaje humano. El objetivo es, a menudo, derivar significado del texto.
Hay muchas aplicaciones diferentes del NLP, entre las cuales se encuentran:
Además, existen numerosas técnicas que se pueden aplicar para analizar texto y extraer significado. Para este mini-proyecto, nos enfocaremos en construir una red neuronal recurrente (RNN) para clasificar el sentimiento de los tweets sobre aerolíneas norteamericanas usando Keras y un Embedding de palabras pre-entrenada.
# This is formatted as codeNuestro conjunto de datos se llama "Twitter US Airline Sentiment", el cual se encuentra en archivo csv en el github del curso. Su fuente original era de la biblioteca de datos de Crowdflower para todos. Los tweets fueron tomados de Twitter en febrero de 2015 de cada una de las principales aerolíneas estadounidenses. Luego, los contribuyentes clasificaron cada tweet como "positivo", "neutral" o "negativo" y citaron el motivo de una clasificación negativa, así como un puntaje de confianza para la etiqueta asignada. El dataset está compuesto por $14,640$ filas y $15$ columnas. Las características incluidas son:
Se utiliza en traduccion de texto y otras aplicaciones donde la salida no tiene una relacion lineal de uno a uno con la salida, en este caso se espera tener todo el vector de entrada y solo cuando se haya ingresado en la red se comienzan a generar la salida. Funciona para traducciones de texto, o tambien para genrar musica a partir de otra musica, etc donde la salida es mas grande o pequeña que la entrada pero necesita que toda la entrada sea procesada
Formas de representar las palabras para que sean mas facil trabajarlas en nlp, generalmente los embedings son redes entrenadas que dan una representacion util
A diferencia de las FFNN, que procesan cada entrada de forma individual e independiente, una RNN crea bucles entre cada nodo en la red neuronal. Esta característica es especialmente provechosa en el caso de datos secuenciales, como textos. Dicha arquitectura es capaz de procesar secuencias y crear un estado que contiene información sobre lo que la red ha visto hasta cierto momento. Esta es la razón por la cual las RNN son útiles para el procesamiento del lenguaje natural, porque las oraciones se decodifican palabra por palabra, manteniendo en memoria las palabras anteriores para dar un mejor contexto para la comprensión. Una RNN permite que la información de una salida anterior se alimente como entrada al estado actual. En pocas palabras, podemos usar información previa para ayudar a tomar una decisión actual.
Las RNN simples sufren el problema del desvanecimiento del gradiente que ocurre cuando la información de las capas anteriores desaparece a medida que la red se vuelve más profunda. Para evitar este problema, se creó el algoritmo LSTM, al permitir que la red neuronal transmitiera información a través de múltiples pasos de tiempo. Esto significa que puede guardar información importante para su uso posterior, evitando que el desvanecimiento del gradiente durante el proceso. Además, una celda LSTM puede determinar qué información eliminar o no. Por lo tanto, puede aprender a reconocer una entrada importante y almacenarla para el futuro mientras elimina información innecesaria.
En NPL, los datos textuales deben representarse de manera que un computador pueda trabajar con ellos. Nos enfocaremos en la técnica de word embeddings, que es una representación de texto donde palabras similares tienen una representación similar. Un modelo de word embedding es word2vec, que toma una gran porción de texto y genera un espacio vectorial donde cada palabra única tiene su propio vector correspondiente. En este espacio, las palabras con significados similares se encuentran cerca una de la otra.
Otro modelo popular es el de los vectores globales para la representación de palabras (GloVe - Gloval Vectors for word representation), que es una extensión de word2vec. Generalmente permite mejores incorporaciones de palabras al crear una matriz de contexto de palabras. Básicamente, crea una medida para indicar que ciertas palabras tienen más probabilidades de verse en el contexto de otras. Por ejemplo, es probable que la palabra "chip" se vea en el contexto de "potato" pero no con "cloud". Sus desarrolladores crearon las incrustaciones utilizando palabras en inglés obtenidas de Wikipedia y datos de rastreo común.
Utilizamos un word embedding de palabras pre-entrenado, ya que se presume que GloVe generaliza los datos con los que trabajaremos. El espacio de embedding creado por GloVe contiene, probablemente, todas las palabras que se encuentran en nuestros tweets, por lo que podemos usar estas representaciones vectoriales en lugar de crear las nuestras a partir de un conjunto de vocabulario mucho más limitado.
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keras.backend.clear_session()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-cde21aa967d0> in <module>()
----> 1 keras.backend.clear_session()
NameError: name 'keras' is not defined
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from tensorflow import keras
import numpy as np
data= """Alégrate de haber reído\n
Del sabor que habitó en tu gusto\n
De las palabras candentes\n
De la siesta dormida en la lluvia\n
De la mano que te soltó una noche\n
De la canción que cantamos un día camino de Sevilla\n
"""
Cuando tenemos un conjunto de palabras, lo primero que queremos hacer es realizar un procesamiento de estos datos, para eso le damos un valor entero que identifique cada palabra de mi texto, de forma tal que podamos convertir la secuencia de palabras en una secuencia de enteros. Buscando tal fin haremos uso de la clase Tokenizer que posee keras para el procesamiento de textos. info
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tok=keras.preprocessing.text.Tokenizer() # le damos un valor numerico a cada palabra
tok.fit_on_texts([data])
Nosotros podemos ver el resultado del tokenizer para ver como quedó definido nuestro diccionario, o la asiganción a cada palabra, de la siguiente forma:
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print(tok.index_word.items())
dict_items([(1, 'de'), (2, 'la'), (3, 'que'), (4, 'en'), (5, 'alégrate'), (6, 'haber'), (7, 'reído'), (8, 'del'), (9, 'sabor'), (10, 'habitó'), (11, 'tu'), (12, 'gusto'), (13, 'las'), (14, 'palabras'), (15, 'candentes'), (16, 'siesta'), (17, 'dormida'), (18, 'lluvia'), (19, 'mano'), (20, 'te'), (21, 'soltó'), (22, 'una'), (23, 'noche'), (24, 'canción'), (25, 'cantamos'), (26, 'un'), (27, 'día'), (28, 'camino'), (29, 'sevilla')])
Ahora pasamos nuestros datos a una secuencia, para eso usamos la herramienta de keras texts_to_sequence, es decir pasamos el párrafo en data a una secuencia.
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sq_enteros=tok.texts_to_sequences([data])[0]
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sq_enteros
Out[7]:
[5,
1,
6,
7,
8,
9,
3,
10,
4,
11,
12,
1,
13,
14,
15,
1,
2,
16,
17,
4,
2,
18,
1,
2,
19,
3,
20,
21,
22,
23,
1,
2,
24,
3,
25,
26,
27,
28,
1,
29]
Como acabamos de ver al hacer el fit con el tokenizer se le asocia a cada palabra un entero y luego con tokenizer.texts_to_sequences regresamos una secuencia correspondiente a nuestro texto. Un valor que debemos tener en cuenta es el del tamaño de nuestro "diccionario", por esto lo guardamos en la siguiente variable.
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con_size = len(tok.word_index) + 1
print(con_size)
30
Ahora, nuestro objetivo será poder predecir la siguiente palabra en la secuencia de acuerdo a la entrada que le demos, por ejemplo, si nuestra entrada es haber, nosotros esperaremos que la salida sea reído, de acuerdo al texto que tenemos. Para esto vamos a crear una secuencia de a dos palabras de acuerdo a neustros datos (input,output), teniendo en cuenta su orden lo cual nos permitirá entrenar nuestro modelo, para eso hacemos lo siguiente:
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secuencia = []
for i in range(1,len(sq_enteros)):
sequence=sq_enteros[i-1:i+1]
secuencia.append(sequence)
print('número de datos en la secuencia:',len(secuencia),'\n')
print(secuencia)
número de datos en la secuencia: 39
[[5, 1], [1, 6], [6, 7], [7, 8], [8, 9], [9, 3], [3, 10], [10, 4], [4, 11], [11, 12], [12, 1], [1, 13], [13, 14], [14, 15], [15, 1], [1, 2], [2, 16], [16, 17], [17, 4], [4, 2], [2, 18], [18, 1], [1, 2], [2, 19], [19, 3], [3, 20], [20, 21], [21, 22], [22, 23], [23, 1], [1, 2], [2, 24], [24, 3], [3, 25], [25, 26], [26, 27], [27, 28], [28, 1], [1, 29]]
Ahora bien los datos anteriores serán los datos que nos permitan entrenar nuestro modelo, para eso dividimos en los datos de entrada y las etiquetas nuestros datos
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secuencia=np.array(secuencia)
X, Y = secuencia[:,0], secuencia[:,1]
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secuencia.shape
Out[11]:
(39, 2)
De acuerdo a nuestro objetivo, con el cual queremos implementar una red en la cual hagamos uso del embedding, debemos hacer lo siguiente:
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y=keras.utils.to_categorical(Y,num_classes=con_size)
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y.shape
Out[14]:
(39, 30)
Donde acabamos de hacer un one-hot-enc a nuestras etiquetas pues deseamos obtener una probabilidad al final asociada a cada palabra de nuestro "diccionario". Ahora bien creamos nuestra red
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# Pasar de un vector de longitud 30(con_size) a algo con 10 dimensiones
# LSTM(50) memoria de corto plazo
model=keras.models.Sequential([
keras.layers.Embedding(con_size,10,input_length=1),
keras.layers.LSTM(50),
keras.layers.Dense(con_size,activation='softmax')
])
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
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model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 1, 10) 300
_________________________________________________________________
lstm (LSTM) (None, 50) 12200
_________________________________________________________________
dense (Dense) (None, 30) 1530
=================================================================
Total params: 14,030
Trainable params: 14,030
Non-trainable params: 0
_________________________________________________________________
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model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
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model.fit(X, y, epochs=500, verbose=2)
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Train on 39 samples
Epoch 1/500
39/39 - 2s - loss: 3.4006 - acc: 0.0769
Epoch 2/500
39/39 - 0s - loss: 3.3989 - acc: 0.1282
Epoch 3/500
39/39 - 0s - loss: 3.3975 - acc: 0.1282
Epoch 4/500
39/39 - 0s - loss: 3.3960 - acc: 0.1538
Epoch 5/500
39/39 - 0s - loss: 3.3947 - acc: 0.1538
Epoch 6/500
39/39 - 0s - loss: 3.3934 - acc: 0.1538
Epoch 7/500
39/39 - 0s - loss: 3.3921 - acc: 0.1538
Epoch 8/500
39/39 - 0s - loss: 3.3907 - acc: 0.1538
Epoch 9/500
39/39 - 0s - loss: 3.3894 - acc: 0.1538
Epoch 10/500
39/39 - 0s - loss: 3.3880 - acc: 0.1538
Epoch 11/500
39/39 - 0s - loss: 3.3867 - acc: 0.1538
Epoch 12/500
39/39 - 0s - loss: 3.3851 - acc: 0.1538
Epoch 13/500
39/39 - 0s - loss: 3.3837 - acc: 0.1538
Epoch 14/500
39/39 - 0s - loss: 3.3821 - acc: 0.1538
Epoch 15/500
39/39 - 0s - loss: 3.3806 - acc: 0.1538
Epoch 16/500
39/39 - 0s - loss: 3.3790 - acc: 0.1538
Epoch 17/500
39/39 - 0s - loss: 3.3773 - acc: 0.1538
Epoch 18/500
39/39 - 0s - loss: 3.3758 - acc: 0.1538
Epoch 19/500
39/39 - 0s - loss: 3.3741 - acc: 0.1538
Epoch 20/500
39/39 - 0s - loss: 3.3723 - acc: 0.1538
Epoch 21/500
39/39 - 0s - loss: 3.3705 - acc: 0.1538
Epoch 22/500
39/39 - 0s - loss: 3.3687 - acc: 0.1538
Epoch 23/500
39/39 - 0s - loss: 3.3665 - acc: 0.1538
Epoch 24/500
39/39 - 0s - loss: 3.3647 - acc: 0.1538
Epoch 25/500
39/39 - 0s - loss: 3.3625 - acc: 0.1538
Epoch 26/500
39/39 - 0s - loss: 3.3602 - acc: 0.1538
Epoch 27/500
39/39 - 0s - loss: 3.3580 - acc: 0.1538
Epoch 28/500
39/39 - 0s - loss: 3.3555 - acc: 0.1538
Epoch 29/500
39/39 - 0s - loss: 3.3530 - acc: 0.1538
Epoch 30/500
39/39 - 0s - loss: 3.3502 - acc: 0.1538
Epoch 31/500
39/39 - 0s - loss: 3.3476 - acc: 0.1538
Epoch 32/500
39/39 - 0s - loss: 3.3447 - acc: 0.1538
Epoch 33/500
39/39 - 0s - loss: 3.3415 - acc: 0.1538
Epoch 34/500
39/39 - 0s - loss: 3.3382 - acc: 0.1538
Epoch 35/500
39/39 - 0s - loss: 3.3349 - acc: 0.1538
Epoch 36/500
39/39 - 0s - loss: 3.3316 - acc: 0.1538
Epoch 37/500
39/39 - 0s - loss: 3.3277 - acc: 0.1538
Epoch 38/500
39/39 - 0s - loss: 3.3240 - acc: 0.1538
Epoch 39/500
39/39 - 0s - loss: 3.3199 - acc: 0.1538
Epoch 40/500
39/39 - 0s - loss: 3.3159 - acc: 0.1538
Epoch 41/500
39/39 - 0s - loss: 3.3114 - acc: 0.1538
Epoch 42/500
39/39 - 0s - loss: 3.3068 - acc: 0.1538
Epoch 43/500
39/39 - 0s - loss: 3.3024 - acc: 0.1538
Epoch 44/500
39/39 - 0s - loss: 3.2973 - acc: 0.1538
Epoch 45/500
39/39 - 0s - loss: 3.2922 - acc: 0.1538
Epoch 46/500
39/39 - 0s - loss: 3.2871 - acc: 0.1538
Epoch 47/500
39/39 - 0s - loss: 3.2813 - acc: 0.1538
Epoch 48/500
39/39 - 0s - loss: 3.2757 - acc: 0.1538
Epoch 49/500
39/39 - 0s - loss: 3.2690 - acc: 0.1538
Epoch 50/500
39/39 - 0s - loss: 3.2635 - acc: 0.1538
Epoch 51/500
39/39 - 0s - loss: 3.2564 - acc: 0.1538
Epoch 52/500
39/39 - 0s - loss: 3.2491 - acc: 0.1538
Epoch 53/500
39/39 - 0s - loss: 3.2422 - acc: 0.1538
Epoch 54/500
39/39 - 0s - loss: 3.2346 - acc: 0.1538
Epoch 55/500
39/39 - 0s - loss: 3.2270 - acc: 0.1538
Epoch 56/500
39/39 - 0s - loss: 3.2186 - acc: 0.1538
Epoch 57/500
39/39 - 0s - loss: 3.2103 - acc: 0.1538
Epoch 58/500
39/39 - 0s - loss: 3.2012 - acc: 0.1538
Epoch 59/500
39/39 - 0s - loss: 3.1924 - acc: 0.1538
Epoch 60/500
39/39 - 0s - loss: 3.1824 - acc: 0.1538
Epoch 61/500
39/39 - 0s - loss: 3.1723 - acc: 0.1538
Epoch 62/500
39/39 - 0s - loss: 3.1618 - acc: 0.1538
Epoch 63/500
39/39 - 0s - loss: 3.1507 - acc: 0.1538
Epoch 64/500
39/39 - 0s - loss: 3.1393 - acc: 0.1538
Epoch 65/500
39/39 - 0s - loss: 3.1271 - acc: 0.1795
Epoch 66/500
39/39 - 0s - loss: 3.1146 - acc: 0.2564
Epoch 67/500
39/39 - 0s - loss: 3.1023 - acc: 0.2564
Epoch 68/500
39/39 - 0s - loss: 3.0879 - acc: 0.2564
Epoch 69/500
39/39 - 0s - loss: 3.0751 - acc: 0.2564
Epoch 70/500
39/39 - 0s - loss: 3.0610 - acc: 0.2564
Epoch 71/500
39/39 - 0s - loss: 3.0468 - acc: 0.2821
Epoch 72/500
39/39 - 0s - loss: 3.0309 - acc: 0.2821
Epoch 73/500
39/39 - 0s - loss: 3.0165 - acc: 0.2821
Epoch 74/500
39/39 - 0s - loss: 3.0015 - acc: 0.2821
Epoch 75/500
39/39 - 0s - loss: 2.9849 - acc: 0.2821
Epoch 76/500
39/39 - 0s - loss: 2.9686 - acc: 0.2821
Epoch 77/500
39/39 - 0s - loss: 2.9519 - acc: 0.2821
Epoch 78/500
39/39 - 0s - loss: 2.9345 - acc: 0.2821
Epoch 79/500
39/39 - 0s - loss: 2.9177 - acc: 0.2821
Epoch 80/500
39/39 - 0s - loss: 2.8996 - acc: 0.3077
Epoch 81/500
39/39 - 0s - loss: 2.8808 - acc: 0.3077
Epoch 82/500
39/39 - 0s - loss: 2.8624 - acc: 0.3077
Epoch 83/500
39/39 - 0s - loss: 2.8426 - acc: 0.3077
Epoch 84/500
39/39 - 0s - loss: 2.8241 - acc: 0.3077
Epoch 85/500
39/39 - 0s - loss: 2.8050 - acc: 0.3077
Epoch 86/500
39/39 - 0s - loss: 2.7839 - acc: 0.3077
Epoch 87/500
39/39 - 0s - loss: 2.7650 - acc: 0.3077
Epoch 88/500
39/39 - 0s - loss: 2.7442 - acc: 0.3077
Epoch 89/500
39/39 - 0s - loss: 2.7254 - acc: 0.3077
Epoch 90/500
39/39 - 0s - loss: 2.7049 - acc: 0.3077
Epoch 91/500
39/39 - 0s - loss: 2.6859 - acc: 0.3077
Epoch 92/500
39/39 - 0s - loss: 2.6668 - acc: 0.3077
Epoch 93/500
39/39 - 0s - loss: 2.6471 - acc: 0.3077
Epoch 94/500
39/39 - 0s - loss: 2.6277 - acc: 0.3077
Epoch 95/500
39/39 - 0s - loss: 2.6090 - acc: 0.3077
Epoch 96/500
39/39 - 0s - loss: 2.5894 - acc: 0.3077
Epoch 97/500
39/39 - 0s - loss: 2.5696 - acc: 0.3077
Epoch 98/500
39/39 - 0s - loss: 2.5500 - acc: 0.3077
Epoch 99/500
39/39 - 0s - loss: 2.5310 - acc: 0.3333
Epoch 100/500
39/39 - 0s - loss: 2.5121 - acc: 0.3333
Epoch 101/500
39/39 - 0s - loss: 2.4926 - acc: 0.3590
Epoch 102/500
39/39 - 0s - loss: 2.4745 - acc: 0.3846
Epoch 103/500
39/39 - 0s - loss: 2.4559 - acc: 0.3846
Epoch 104/500
39/39 - 0s - loss: 2.4376 - acc: 0.3846
Epoch 105/500
39/39 - 0s - loss: 2.4215 - acc: 0.3846
Epoch 106/500
39/39 - 0s - loss: 2.4031 - acc: 0.3846
Epoch 107/500
39/39 - 0s - loss: 2.3866 - acc: 0.3846
Epoch 108/500
39/39 - 0s - loss: 2.3703 - acc: 0.3846
Epoch 109/500
39/39 - 0s - loss: 2.3538 - acc: 0.3846
Epoch 110/500
39/39 - 0s - loss: 2.3379 - acc: 0.3846
Epoch 111/500
39/39 - 0s - loss: 2.3223 - acc: 0.3846
Epoch 112/500
39/39 - 0s - loss: 2.3066 - acc: 0.3846
Epoch 113/500
39/39 - 0s - loss: 2.2912 - acc: 0.3846
Epoch 114/500
39/39 - 0s - loss: 2.2760 - acc: 0.3846
Epoch 115/500
39/39 - 0s - loss: 2.2611 - acc: 0.3846
Epoch 116/500
39/39 - 0s - loss: 2.2465 - acc: 0.3846
Epoch 117/500
39/39 - 0s - loss: 2.2317 - acc: 0.3846
Epoch 118/500
39/39 - 0s - loss: 2.2172 - acc: 0.3846
Epoch 119/500
39/39 - 0s - loss: 2.2030 - acc: 0.3846
Epoch 120/500
39/39 - 0s - loss: 2.1890 - acc: 0.3846
Epoch 121/500
39/39 - 0s - loss: 2.1749 - acc: 0.3846
Epoch 122/500
39/39 - 0s - loss: 2.1607 - acc: 0.3846
Epoch 123/500
39/39 - 0s - loss: 2.1466 - acc: 0.3846
Epoch 124/500
39/39 - 0s - loss: 2.1334 - acc: 0.3846
Epoch 125/500
39/39 - 0s - loss: 2.1199 - acc: 0.3846
Epoch 126/500
39/39 - 0s - loss: 2.1059 - acc: 0.3846
Epoch 127/500
39/39 - 0s - loss: 2.0928 - acc: 0.3846
Epoch 128/500
39/39 - 0s - loss: 2.0792 - acc: 0.3846
Epoch 129/500
39/39 - 0s - loss: 2.0657 - acc: 0.3846
Epoch 130/500
39/39 - 0s - loss: 2.0532 - acc: 0.3846
Epoch 131/500
39/39 - 0s - loss: 2.0392 - acc: 0.3846
Epoch 132/500
39/39 - 0s - loss: 2.0267 - acc: 0.3846
Epoch 133/500
39/39 - 0s - loss: 2.0136 - acc: 0.3846
Epoch 134/500
39/39 - 0s - loss: 2.0010 - acc: 0.3846
Epoch 135/500
39/39 - 0s - loss: 1.9884 - acc: 0.3846
Epoch 136/500
39/39 - 0s - loss: 1.9757 - acc: 0.3846
Epoch 137/500
39/39 - 0s - loss: 1.9635 - acc: 0.3846
Epoch 138/500
39/39 - 0s - loss: 1.9513 - acc: 0.3846
Epoch 139/500
39/39 - 0s - loss: 1.9394 - acc: 0.3846
Epoch 140/500
39/39 - 0s - loss: 1.9272 - acc: 0.4103
Epoch 141/500
39/39 - 0s - loss: 1.9159 - acc: 0.4103
Epoch 142/500
39/39 - 0s - loss: 1.9043 - acc: 0.4103
Epoch 143/500
39/39 - 0s - loss: 1.8925 - acc: 0.4103
Epoch 144/500
39/39 - 0s - loss: 1.8815 - acc: 0.4103
Epoch 145/500
39/39 - 0s - loss: 1.8698 - acc: 0.4103
Epoch 146/500
39/39 - 0s - loss: 1.8583 - acc: 0.4103
Epoch 147/500
39/39 - 0s - loss: 1.8469 - acc: 0.4103
Epoch 148/500
39/39 - 0s - loss: 1.8351 - acc: 0.4103
Epoch 149/500
39/39 - 0s - loss: 1.8237 - acc: 0.4103
Epoch 150/500
39/39 - 0s - loss: 1.8116 - acc: 0.4103
Epoch 151/500
39/39 - 0s - loss: 1.8006 - acc: 0.4103
Epoch 152/500
39/39 - 0s - loss: 1.7891 - acc: 0.4103
Epoch 153/500
39/39 - 0s - loss: 1.7774 - acc: 0.4103
Epoch 154/500
39/39 - 0s - loss: 1.7655 - acc: 0.4103
Epoch 155/500
39/39 - 0s - loss: 1.7546 - acc: 0.4103
Epoch 156/500
39/39 - 0s - loss: 1.7430 - acc: 0.4103
Epoch 157/500
39/39 - 0s - loss: 1.7315 - acc: 0.4103
Epoch 158/500
39/39 - 0s - loss: 1.7206 - acc: 0.4103
Epoch 159/500
39/39 - 0s - loss: 1.7094 - acc: 0.4103
Epoch 160/500
39/39 - 0s - loss: 1.6981 - acc: 0.4359
Epoch 161/500
39/39 - 0s - loss: 1.6875 - acc: 0.4359
Epoch 162/500
39/39 - 0s - loss: 1.6755 - acc: 0.4359
Epoch 163/500
39/39 - 0s - loss: 1.6647 - acc: 0.4359
Epoch 164/500
39/39 - 0s - loss: 1.6539 - acc: 0.4359
Epoch 165/500
39/39 - 0s - loss: 1.6426 - acc: 0.4359
Epoch 166/500
39/39 - 0s - loss: 1.6320 - acc: 0.4359
Epoch 167/500
39/39 - 0s - loss: 1.6211 - acc: 0.4359
Epoch 168/500
39/39 - 0s - loss: 1.6101 - acc: 0.4615
Epoch 169/500
39/39 - 0s - loss: 1.5995 - acc: 0.4615
Epoch 170/500
39/39 - 0s - loss: 1.5889 - acc: 0.4872
Epoch 171/500
39/39 - 0s - loss: 1.5786 - acc: 0.4872
Epoch 172/500
39/39 - 0s - loss: 1.5682 - acc: 0.5128
Epoch 173/500
39/39 - 0s - loss: 1.5580 - acc: 0.4872
Epoch 174/500
39/39 - 0s - loss: 1.5470 - acc: 0.4872
Epoch 175/500
39/39 - 0s - loss: 1.5368 - acc: 0.4872
Epoch 176/500
39/39 - 0s - loss: 1.5263 - acc: 0.4872
Epoch 177/500
39/39 - 0s - loss: 1.5162 - acc: 0.5385
Epoch 178/500
39/39 - 0s - loss: 1.5055 - acc: 0.5641
Epoch 179/500
39/39 - 0s - loss: 1.4959 - acc: 0.5641
Epoch 180/500
39/39 - 0s - loss: 1.4859 - acc: 0.5641
Epoch 181/500
39/39 - 0s - loss: 1.4752 - acc: 0.5641
Epoch 182/500
39/39 - 0s - loss: 1.4657 - acc: 0.5641
Epoch 183/500
39/39 - 0s - loss: 1.4557 - acc: 0.5641
Epoch 184/500
39/39 - 0s - loss: 1.4460 - acc: 0.5641
Epoch 185/500
39/39 - 0s - loss: 1.4371 - acc: 0.5641
Epoch 186/500
39/39 - 0s - loss: 1.4273 - acc: 0.5641
Epoch 187/500
39/39 - 0s - loss: 1.4183 - acc: 0.5641
Epoch 188/500
39/39 - 0s - loss: 1.4090 - acc: 0.5641
Epoch 189/500
39/39 - 0s - loss: 1.4004 - acc: 0.5641
Epoch 190/500
39/39 - 0s - loss: 1.3909 - acc: 0.5897
Epoch 191/500
39/39 - 0s - loss: 1.3825 - acc: 0.5897
Epoch 192/500
39/39 - 0s - loss: 1.3733 - acc: 0.5897
Epoch 193/500
39/39 - 0s - loss: 1.3649 - acc: 0.5897
Epoch 194/500
39/39 - 0s - loss: 1.3561 - acc: 0.5897
Epoch 195/500
39/39 - 0s - loss: 1.3474 - acc: 0.5897
Epoch 196/500
39/39 - 0s - loss: 1.3385 - acc: 0.5897
Epoch 197/500
39/39 - 0s - loss: 1.3303 - acc: 0.5897
Epoch 198/500
39/39 - 0s - loss: 1.3211 - acc: 0.6154
Epoch 199/500
39/39 - 0s - loss: 1.3126 - acc: 0.6154
Epoch 200/500
39/39 - 0s - loss: 1.3037 - acc: 0.6154
Epoch 201/500
39/39 - 0s - loss: 1.2959 - acc: 0.6154
Epoch 202/500
39/39 - 0s - loss: 1.2873 - acc: 0.6154
Epoch 203/500
39/39 - 0s - loss: 1.2785 - acc: 0.6154
Epoch 204/500
39/39 - 0s - loss: 1.2705 - acc: 0.6154
Epoch 205/500
39/39 - 0s - loss: 1.2620 - acc: 0.6154
Epoch 206/500
39/39 - 0s - loss: 1.2540 - acc: 0.6154
Epoch 207/500
39/39 - 0s - loss: 1.2455 - acc: 0.6154
Epoch 208/500
39/39 - 0s - loss: 1.2376 - acc: 0.6154
Epoch 209/500
39/39 - 0s - loss: 1.2289 - acc: 0.6154
Epoch 210/500
39/39 - 0s - loss: 1.2209 - acc: 0.6154
Epoch 211/500
39/39 - 0s - loss: 1.2131 - acc: 0.6410
Epoch 212/500
39/39 - 0s - loss: 1.2043 - acc: 0.6667
Epoch 213/500
39/39 - 0s - loss: 1.1968 - acc: 0.6923
Epoch 214/500
39/39 - 0s - loss: 1.1882 - acc: 0.6923
Epoch 215/500
39/39 - 0s - loss: 1.1805 - acc: 0.6923
Epoch 216/500
39/39 - 0s - loss: 1.1730 - acc: 0.6923
Epoch 217/500
39/39 - 0s - loss: 1.1644 - acc: 0.6923
Epoch 218/500
39/39 - 0s - loss: 1.1574 - acc: 0.7179
Epoch 219/500
39/39 - 0s - loss: 1.1505 - acc: 0.7179
Epoch 220/500
39/39 - 0s - loss: 1.1426 - acc: 0.7179
Epoch 221/500
39/39 - 0s - loss: 1.1355 - acc: 0.7179
Epoch 222/500
39/39 - 0s - loss: 1.1281 - acc: 0.7179
Epoch 223/500
39/39 - 0s - loss: 1.1214 - acc: 0.7179
Epoch 224/500
39/39 - 0s - loss: 1.1137 - acc: 0.7179
Epoch 225/500
39/39 - 0s - loss: 1.1068 - acc: 0.7179
Epoch 226/500
39/39 - 0s - loss: 1.0997 - acc: 0.7179
Epoch 227/500
39/39 - 0s - loss: 1.0922 - acc: 0.7179
Epoch 228/500
39/39 - 0s - loss: 1.0852 - acc: 0.7179
Epoch 229/500
39/39 - 0s - loss: 1.0786 - acc: 0.7179
Epoch 230/500
39/39 - 0s - loss: 1.0709 - acc: 0.7179
Epoch 231/500
39/39 - 0s - loss: 1.0643 - acc: 0.7179
Epoch 232/500
39/39 - 0s - loss: 1.0575 - acc: 0.7179
Epoch 233/500
39/39 - 0s - loss: 1.0509 - acc: 0.7179
Epoch 234/500
39/39 - 0s - loss: 1.0438 - acc: 0.7179
Epoch 235/500
39/39 - 0s - loss: 1.0371 - acc: 0.7179
Epoch 236/500
39/39 - 0s - loss: 1.0307 - acc: 0.7179
Epoch 237/500
39/39 - 0s - loss: 1.0240 - acc: 0.7179
Epoch 238/500
39/39 - 0s - loss: 1.0175 - acc: 0.7179
Epoch 239/500
39/39 - 0s - loss: 1.0106 - acc: 0.7179
Epoch 240/500
39/39 - 0s - loss: 1.0042 - acc: 0.7179
Epoch 241/500
39/39 - 0s - loss: 0.9973 - acc: 0.7179
Epoch 242/500
39/39 - 0s - loss: 0.9908 - acc: 0.7179
Epoch 243/500
39/39 - 0s - loss: 0.9854 - acc: 0.7179
Epoch 244/500
39/39 - 0s - loss: 0.9786 - acc: 0.7436
Epoch 245/500
39/39 - 0s - loss: 0.9731 - acc: 0.7436
Epoch 246/500
39/39 - 0s - loss: 0.9674 - acc: 0.7436
Epoch 247/500
39/39 - 0s - loss: 0.9617 - acc: 0.7436
Epoch 248/500
39/39 - 0s - loss: 0.9556 - acc: 0.7436
Epoch 249/500
39/39 - 0s - loss: 0.9500 - acc: 0.7436
Epoch 250/500
39/39 - 0s - loss: 0.9441 - acc: 0.7436
Epoch 251/500
39/39 - 0s - loss: 0.9383 - acc: 0.7436
Epoch 252/500
39/39 - 0s - loss: 0.9329 - acc: 0.7436
Epoch 253/500
39/39 - 0s - loss: 0.9275 - acc: 0.7436
Epoch 254/500
39/39 - 0s - loss: 0.9218 - acc: 0.7436
Epoch 255/500
39/39 - 0s - loss: 0.9165 - acc: 0.7436
Epoch 256/500
39/39 - 0s - loss: 0.9112 - acc: 0.7436
Epoch 257/500
39/39 - 0s - loss: 0.9062 - acc: 0.7436
Epoch 258/500
39/39 - 0s - loss: 0.9013 - acc: 0.7436
Epoch 259/500
39/39 - 0s - loss: 0.8961 - acc: 0.7436
Epoch 260/500
39/39 - 0s - loss: 0.8912 - acc: 0.7436
Epoch 261/500
39/39 - 0s - loss: 0.8862 - acc: 0.7436
Epoch 262/500
39/39 - 0s - loss: 0.8809 - acc: 0.7436
Epoch 263/500
39/39 - 0s - loss: 0.8763 - acc: 0.7436
Epoch 264/500
39/39 - 0s - loss: 0.8712 - acc: 0.7436
Epoch 265/500
39/39 - 0s - loss: 0.8659 - acc: 0.7436
Epoch 266/500
39/39 - 0s - loss: 0.8614 - acc: 0.7436
Epoch 267/500
39/39 - 0s - loss: 0.8561 - acc: 0.7436
Epoch 268/500
39/39 - 0s - loss: 0.8518 - acc: 0.7692
Epoch 269/500
39/39 - 0s - loss: 0.8470 - acc: 0.7692
Epoch 270/500
39/39 - 0s - loss: 0.8423 - acc: 0.7692
Epoch 271/500
39/39 - 0s - loss: 0.8379 - acc: 0.7692
Epoch 272/500
39/39 - 0s - loss: 0.8336 - acc: 0.7692
Epoch 273/500
39/39 - 0s - loss: 0.8289 - acc: 0.7692
Epoch 274/500
39/39 - 0s - loss: 0.8246 - acc: 0.7692
Epoch 275/500
39/39 - 0s - loss: 0.8204 - acc: 0.7692
Epoch 276/500
39/39 - 0s - loss: 0.8159 - acc: 0.7692
Epoch 277/500
39/39 - 0s - loss: 0.8111 - acc: 0.7692
Epoch 278/500
39/39 - 0s - loss: 0.8068 - acc: 0.7692
Epoch 279/500
39/39 - 0s - loss: 0.8021 - acc: 0.7692
Epoch 280/500
39/39 - 0s - loss: 0.7976 - acc: 0.7692
Epoch 281/500
39/39 - 0s - loss: 0.7938 - acc: 0.7692
Epoch 282/500
39/39 - 0s - loss: 0.7898 - acc: 0.7692
Epoch 283/500
39/39 - 0s - loss: 0.7864 - acc: 0.7692
Epoch 284/500
39/39 - 0s - loss: 0.7819 - acc: 0.7692
Epoch 285/500
39/39 - 0s - loss: 0.7777 - acc: 0.7692
Epoch 286/500
39/39 - 0s - loss: 0.7741 - acc: 0.7692
Epoch 287/500
39/39 - 0s - loss: 0.7698 - acc: 0.7692
Epoch 288/500
39/39 - 0s - loss: 0.7660 - acc: 0.7692
Epoch 289/500
39/39 - 0s - loss: 0.7620 - acc: 0.7692
Epoch 290/500
39/39 - 0s - loss: 0.7585 - acc: 0.7692
Epoch 291/500
39/39 - 0s - loss: 0.7548 - acc: 0.7692
Epoch 292/500
39/39 - 0s - loss: 0.7513 - acc: 0.7692
Epoch 293/500
39/39 - 0s - loss: 0.7479 - acc: 0.7692
Epoch 294/500
39/39 - 0s - loss: 0.7447 - acc: 0.7692
Epoch 295/500
39/39 - 0s - loss: 0.7413 - acc: 0.7692
Epoch 296/500
39/39 - 0s - loss: 0.7385 - acc: 0.7692
Epoch 297/500
39/39 - 0s - loss: 0.7345 - acc: 0.7692
Epoch 298/500
39/39 - 0s - loss: 0.7314 - acc: 0.7692
Epoch 299/500
39/39 - 0s - loss: 0.7276 - acc: 0.7692
Epoch 300/500
39/39 - 0s - loss: 0.7245 - acc: 0.7692
Epoch 301/500
39/39 - 0s - loss: 0.7214 - acc: 0.7692
Epoch 302/500
39/39 - 0s - loss: 0.7182 - acc: 0.7692
Epoch 303/500
39/39 - 0s - loss: 0.7154 - acc: 0.7692
Epoch 304/500
39/39 - 0s - loss: 0.7122 - acc: 0.7692
Epoch 305/500
39/39 - 0s - loss: 0.7095 - acc: 0.7692
Epoch 306/500
39/39 - 0s - loss: 0.7063 - acc: 0.7692
Epoch 307/500
39/39 - 0s - loss: 0.7038 - acc: 0.7692
Epoch 308/500
39/39 - 0s - loss: 0.7008 - acc: 0.7692
Epoch 309/500
39/39 - 0s - loss: 0.6976 - acc: 0.7692
Epoch 310/500
39/39 - 0s - loss: 0.6946 - acc: 0.7692
Epoch 311/500
39/39 - 0s - loss: 0.6922 - acc: 0.7692
Epoch 312/500
39/39 - 0s - loss: 0.6889 - acc: 0.7692
Epoch 313/500
39/39 - 0s - loss: 0.6860 - acc: 0.7692
Epoch 314/500
39/39 - 0s - loss: 0.6828 - acc: 0.7692
Epoch 315/500
39/39 - 0s - loss: 0.6798 - acc: 0.7692
Epoch 316/500
39/39 - 0s - loss: 0.6773 - acc: 0.7692
Epoch 317/500
39/39 - 0s - loss: 0.6749 - acc: 0.7692
Epoch 318/500
39/39 - 0s - loss: 0.6724 - acc: 0.7692
Epoch 319/500
39/39 - 0s - loss: 0.6688 - acc: 0.7692
Epoch 320/500
39/39 - 0s - loss: 0.6660 - acc: 0.7692
Epoch 321/500
39/39 - 0s - loss: 0.6633 - acc: 0.7692
Epoch 322/500
39/39 - 0s - loss: 0.6611 - acc: 0.7692
Epoch 323/500
39/39 - 0s - loss: 0.6582 - acc: 0.7692
Epoch 324/500
39/39 - 0s - loss: 0.6556 - acc: 0.7692
Epoch 325/500
39/39 - 0s - loss: 0.6528 - acc: 0.7692
Epoch 326/500
39/39 - 0s - loss: 0.6508 - acc: 0.7692
Epoch 327/500
39/39 - 0s - loss: 0.6486 - acc: 0.7692
Epoch 328/500
39/39 - 0s - loss: 0.6458 - acc: 0.7692
Epoch 329/500
39/39 - 0s - loss: 0.6439 - acc: 0.7692
Epoch 330/500
39/39 - 0s - loss: 0.6422 - acc: 0.7692
Epoch 331/500
39/39 - 0s - loss: 0.6399 - acc: 0.7692
Epoch 332/500
39/39 - 0s - loss: 0.6379 - acc: 0.7692
Epoch 333/500
39/39 - 0s - loss: 0.6359 - acc: 0.7692
Epoch 334/500
39/39 - 0s - loss: 0.6338 - acc: 0.7692
Epoch 335/500
39/39 - 0s - loss: 0.6320 - acc: 0.7692
Epoch 336/500
39/39 - 0s - loss: 0.6297 - acc: 0.7692
Epoch 337/500
39/39 - 0s - loss: 0.6279 - acc: 0.7692
Epoch 338/500
39/39 - 0s - loss: 0.6258 - acc: 0.7692
Epoch 339/500
39/39 - 0s - loss: 0.6241 - acc: 0.7692
Epoch 340/500
39/39 - 0s - loss: 0.6223 - acc: 0.7692
Epoch 341/500
39/39 - 0s - loss: 0.6204 - acc: 0.7692
Epoch 342/500
39/39 - 0s - loss: 0.6185 - acc: 0.7692
Epoch 343/500
39/39 - 0s - loss: 0.6167 - acc: 0.7692
Epoch 344/500
39/39 - 0s - loss: 0.6148 - acc: 0.7692
Epoch 345/500
39/39 - 0s - loss: 0.6130 - acc: 0.7692
Epoch 346/500
39/39 - 0s - loss: 0.6115 - acc: 0.7692
Epoch 347/500
39/39 - 0s - loss: 0.6099 - acc: 0.7692
Epoch 348/500
39/39 - 0s - loss: 0.6088 - acc: 0.7692
Epoch 349/500
39/39 - 0s - loss: 0.6072 - acc: 0.7692
Epoch 350/500
39/39 - 0s - loss: 0.6054 - acc: 0.7692
Epoch 351/500
39/39 - 0s - loss: 0.6037 - acc: 0.7692
Epoch 352/500
39/39 - 0s - loss: 0.6019 - acc: 0.7692
Epoch 353/500
39/39 - 0s - loss: 0.6005 - acc: 0.7692
Epoch 354/500
39/39 - 0s - loss: 0.5990 - acc: 0.7692
Epoch 355/500
39/39 - 0s - loss: 0.5974 - acc: 0.7692
Epoch 356/500
39/39 - 0s - loss: 0.5957 - acc: 0.7692
Epoch 357/500
39/39 - 0s - loss: 0.5941 - acc: 0.7692
Epoch 358/500
39/39 - 0s - loss: 0.5924 - acc: 0.7692
Epoch 359/500
39/39 - 0s - loss: 0.5912 - acc: 0.7692
Epoch 360/500
39/39 - 0s - loss: 0.5902 - acc: 0.7692
Epoch 361/500
39/39 - 0s - loss: 0.5882 - acc: 0.7692
Epoch 362/500
39/39 - 0s - loss: 0.5868 - acc: 0.7692
Epoch 363/500
39/39 - 0s - loss: 0.5855 - acc: 0.7692
Epoch 364/500
39/39 - 0s - loss: 0.5845 - acc: 0.7692
Epoch 365/500
39/39 - 0s - loss: 0.5828 - acc: 0.7692
Epoch 366/500
39/39 - 0s - loss: 0.5814 - acc: 0.7692
Epoch 367/500
39/39 - 0s - loss: 0.5801 - acc: 0.7692
Epoch 368/500
39/39 - 0s - loss: 0.5795 - acc: 0.7692
Epoch 369/500
39/39 - 0s - loss: 0.5785 - acc: 0.7692
Epoch 370/500
39/39 - 0s - loss: 0.5782 - acc: 0.7692
Epoch 371/500
39/39 - 0s - loss: 0.5764 - acc: 0.7692
Epoch 372/500
39/39 - 0s - loss: 0.5752 - acc: 0.7692
Epoch 373/500
39/39 - 0s - loss: 0.5737 - acc: 0.7692
Epoch 374/500
39/39 - 0s - loss: 0.5724 - acc: 0.7692
Epoch 375/500
39/39 - 0s - loss: 0.5707 - acc: 0.7692
Epoch 376/500
39/39 - 0s - loss: 0.5697 - acc: 0.7692
Epoch 377/500
39/39 - 0s - loss: 0.5685 - acc: 0.7692
Epoch 378/500
39/39 - 0s - loss: 0.5669 - acc: 0.7692
Epoch 379/500
39/39 - 0s - loss: 0.5663 - acc: 0.7692
Epoch 380/500
39/39 - 0s - loss: 0.5647 - acc: 0.7692
Epoch 381/500
39/39 - 0s - loss: 0.5638 - acc: 0.7692
Epoch 382/500
39/39 - 0s - loss: 0.5629 - acc: 0.7692
Epoch 383/500
39/39 - 0s - loss: 0.5613 - acc: 0.7692
Epoch 384/500
39/39 - 0s - loss: 0.5608 - acc: 0.7692
Epoch 385/500
39/39 - 0s - loss: 0.5595 - acc: 0.7692
Epoch 386/500
39/39 - 0s - loss: 0.5585 - acc: 0.7692
Epoch 387/500
39/39 - 0s - loss: 0.5575 - acc: 0.7692
Epoch 388/500
39/39 - 0s - loss: 0.5560 - acc: 0.7692
Epoch 389/500
39/39 - 0s - loss: 0.5553 - acc: 0.7692
Epoch 390/500
39/39 - 0s - loss: 0.5541 - acc: 0.7692
Epoch 391/500
39/39 - 0s - loss: 0.5533 - acc: 0.7692
Epoch 392/500
39/39 - 0s - loss: 0.5524 - acc: 0.7692
Epoch 393/500
39/39 - 0s - loss: 0.5513 - acc: 0.7692
Epoch 394/500
39/39 - 0s - loss: 0.5500 - acc: 0.7692
Epoch 395/500
39/39 - 0s - loss: 0.5495 - acc: 0.7692
Epoch 396/500
39/39 - 0s - loss: 0.5486 - acc: 0.7692
Epoch 397/500
39/39 - 0s - loss: 0.5475 - acc: 0.7692
Epoch 398/500
39/39 - 0s - loss: 0.5464 - acc: 0.7692
Epoch 399/500
39/39 - 0s - loss: 0.5456 - acc: 0.7692
Epoch 400/500
39/39 - 0s - loss: 0.5450 - acc: 0.7692
Epoch 401/500
39/39 - 0s - loss: 0.5436 - acc: 0.7692
Epoch 402/500
39/39 - 0s - loss: 0.5421 - acc: 0.7692
Epoch 403/500
39/39 - 0s - loss: 0.5412 - acc: 0.7692
Epoch 404/500
39/39 - 0s - loss: 0.5414 - acc: 0.7692
Epoch 405/500
39/39 - 0s - loss: 0.5406 - acc: 0.7692
Epoch 406/500
39/39 - 0s - loss: 0.5395 - acc: 0.7692
Epoch 407/500
39/39 - 0s - loss: 0.5391 - acc: 0.7692
Epoch 408/500
39/39 - 0s - loss: 0.5394 - acc: 0.7692
Epoch 409/500
39/39 - 0s - loss: 0.5378 - acc: 0.7692
Epoch 410/500
39/39 - 0s - loss: 0.5370 - acc: 0.7692
Epoch 411/500
39/39 - 0s - loss: 0.5361 - acc: 0.7692
Epoch 412/500
39/39 - 0s - loss: 0.5352 - acc: 0.7692
Epoch 413/500
39/39 - 0s - loss: 0.5346 - acc: 0.7692
Epoch 414/500
39/39 - 0s - loss: 0.5338 - acc: 0.7692
Epoch 415/500
39/39 - 0s - loss: 0.5327 - acc: 0.7692
Epoch 416/500
39/39 - 0s - loss: 0.5324 - acc: 0.7692
Epoch 417/500
39/39 - 0s - loss: 0.5314 - acc: 0.7692
Epoch 418/500
39/39 - 0s - loss: 0.5306 - acc: 0.7692
Epoch 419/500
39/39 - 0s - loss: 0.5300 - acc: 0.7692
Epoch 420/500
39/39 - 0s - loss: 0.5292 - acc: 0.7692
Epoch 421/500
39/39 - 0s - loss: 0.5286 - acc: 0.7692
Epoch 422/500
39/39 - 0s - loss: 0.5282 - acc: 0.7692
Epoch 423/500
39/39 - 0s - loss: 0.5273 - acc: 0.7692
Epoch 424/500
39/39 - 0s - loss: 0.5267 - acc: 0.7692
Epoch 425/500
39/39 - 0s - loss: 0.5261 - acc: 0.7692
Epoch 426/500
39/39 - 0s - loss: 0.5250 - acc: 0.7692
Epoch 427/500
39/39 - 0s - loss: 0.5244 - acc: 0.7692
Epoch 428/500
39/39 - 0s - loss: 0.5245 - acc: 0.7692
Epoch 429/500
39/39 - 0s - loss: 0.5238 - acc: 0.7692
Epoch 430/500
39/39 - 0s - loss: 0.5238 - acc: 0.7692
Epoch 431/500
39/39 - 0s - loss: 0.5234 - acc: 0.7949
Epoch 432/500
39/39 - 0s - loss: 0.5229 - acc: 0.7692
Epoch 433/500
39/39 - 0s - loss: 0.5224 - acc: 0.7692
Epoch 434/500
39/39 - 0s - loss: 0.5222 - acc: 0.7692
Epoch 435/500
39/39 - 0s - loss: 0.5215 - acc: 0.7692
Epoch 436/500
39/39 - 0s - loss: 0.5209 - acc: 0.7692
Epoch 437/500
39/39 - 0s - loss: 0.5202 - acc: 0.7692
Epoch 438/500
39/39 - 0s - loss: 0.5199 - acc: 0.7692
Epoch 439/500
39/39 - 0s - loss: 0.5198 - acc: 0.7692
Epoch 440/500
39/39 - 0s - loss: 0.5193 - acc: 0.7692
Epoch 441/500
39/39 - 0s - loss: 0.5187 - acc: 0.7692
Epoch 442/500
39/39 - 0s - loss: 0.5180 - acc: 0.7692
Epoch 443/500
39/39 - 0s - loss: 0.5175 - acc: 0.7692
Epoch 444/500
39/39 - 0s - loss: 0.5169 - acc: 0.7692
Epoch 445/500
39/39 - 0s - loss: 0.5166 - acc: 0.7692
Epoch 446/500
39/39 - 0s - loss: 0.5161 - acc: 0.7692
Epoch 447/500
39/39 - 0s - loss: 0.5153 - acc: 0.7692
Epoch 448/500
39/39 - 0s - loss: 0.5146 - acc: 0.7692
Epoch 449/500
39/39 - 0s - loss: 0.5141 - acc: 0.7692
Epoch 450/500
39/39 - 0s - loss: 0.5137 - acc: 0.7692
Epoch 451/500
39/39 - 0s - loss: 0.5135 - acc: 0.7692
Epoch 452/500
39/39 - 0s - loss: 0.5132 - acc: 0.7692
Epoch 453/500
39/39 - 0s - loss: 0.5125 - acc: 0.7692
Epoch 454/500
39/39 - 0s - loss: 0.5128 - acc: 0.7692
Epoch 455/500
39/39 - 0s - loss: 0.5119 - acc: 0.7692
Epoch 456/500
39/39 - 0s - loss: 0.5114 - acc: 0.7692
Epoch 457/500
39/39 - 0s - loss: 0.5110 - acc: 0.7692
Epoch 458/500
39/39 - 0s - loss: 0.5103 - acc: 0.7692
Epoch 459/500
39/39 - 0s - loss: 0.5107 - acc: 0.7692
Epoch 460/500
39/39 - 0s - loss: 0.5103 - acc: 0.7692
Epoch 461/500
39/39 - 0s - loss: 0.5107 - acc: 0.7692
Epoch 462/500
39/39 - 0s - loss: 0.5097 - acc: 0.7692
Epoch 463/500
39/39 - 0s - loss: 0.5092 - acc: 0.7692
Epoch 464/500
39/39 - 0s - loss: 0.5091 - acc: 0.7692
Epoch 465/500
39/39 - 0s - loss: 0.5087 - acc: 0.7692
Epoch 466/500
39/39 - 0s - loss: 0.5079 - acc: 0.7692
Epoch 467/500
39/39 - 0s - loss: 0.5075 - acc: 0.7692
Epoch 468/500
39/39 - 0s - loss: 0.5075 - acc: 0.7692
Epoch 469/500
39/39 - 0s - loss: 0.5067 - acc: 0.7692
Epoch 470/500
39/39 - 0s - loss: 0.5064 - acc: 0.7692
Epoch 471/500
39/39 - 0s - loss: 0.5058 - acc: 0.7692
Epoch 472/500
39/39 - 0s - loss: 0.5055 - acc: 0.7692
Epoch 473/500
39/39 - 0s - loss: 0.5056 - acc: 0.7692
Epoch 474/500
39/39 - 0s - loss: 0.5051 - acc: 0.7692
Epoch 475/500
39/39 - 0s - loss: 0.5050 - acc: 0.7692
Epoch 476/500
39/39 - 0s - loss: 0.5051 - acc: 0.7692
Epoch 477/500
39/39 - 0s - loss: 0.5046 - acc: 0.7692
Epoch 478/500
39/39 - 0s - loss: 0.5042 - acc: 0.7692
Epoch 479/500
39/39 - 0s - loss: 0.5037 - acc: 0.7692
Epoch 480/500
39/39 - 0s - loss: 0.5039 - acc: 0.7692
Epoch 481/500
39/39 - 0s - loss: 0.5034 - acc: 0.7692
Epoch 482/500
39/39 - 0s - loss: 0.5028 - acc: 0.7692
Epoch 483/500
39/39 - 0s - loss: 0.5024 - acc: 0.7692
Epoch 484/500
39/39 - 0s - loss: 0.5018 - acc: 0.7692
Epoch 485/500
39/39 - 0s - loss: 0.5011 - acc: 0.7692
Epoch 486/500
39/39 - 0s - loss: 0.5010 - acc: 0.7692
Epoch 487/500
39/39 - 0s - loss: 0.5004 - acc: 0.7692
Epoch 488/500
39/39 - 0s - loss: 0.5003 - acc: 0.7692
Epoch 489/500
39/39 - 0s - loss: 0.4997 - acc: 0.7692
Epoch 490/500
39/39 - 0s - loss: 0.4992 - acc: 0.7692
Epoch 491/500
39/39 - 0s - loss: 0.4987 - acc: 0.7692
Epoch 492/500
39/39 - 0s - loss: 0.4986 - acc: 0.7692
Epoch 493/500
39/39 - 0s - loss: 0.4984 - acc: 0.7692
Epoch 494/500
39/39 - 0s - loss: 0.4982 - acc: 0.7692
Epoch 495/500
39/39 - 0s - loss: 0.4976 - acc: 0.7692
Epoch 496/500
39/39 - 0s - loss: 0.4977 - acc: 0.7692
Epoch 497/500
39/39 - 0s - loss: 0.4972 - acc: 0.7692
Epoch 498/500
39/39 - 0s - loss: 0.4971 - acc: 0.7436
Epoch 499/500
39/39 - 0s - loss: 0.4970 - acc: 0.7692
Epoch 500/500
39/39 - 0s - loss: 0.4975 - acc: 0.7692
Out[18]:
<tensorflow.python.keras.callbacks.History at 0x7f63a06dab70>
Finalmente, probemos nuestro modelo
In [19]:
in_text = 'haber'
print(in_text)
encoded = tok.texts_to_sequences([in_text])[0]
encoded = np.array(encoded)
yhat = model.predict_classes(encoded, verbose=0)
for word, index in tok.word_index.items():
if index == yhat:
print(word)
haber
reído
Donde es el resultado esperado de acuerdo a nuestro texto. Ahora iteremos varias veces para predecir una secuencia de palabras o frase, para esto construímos la siguiente función que nos permitirá hacer esto a partir de una palbra inicial y el número de palabras que buscamos predecir.
In [0]:
def generate_seq(model, tok, seed_text, n_words):
in_text, result = seed_text, seed_text
for _ in range(n_words):
encoded = tok.texts_to_sequences([in_text])[0]
encoded = np.array(encoded)
yhat = model.predict_classes(encoded, verbose=0)
out_word = ''
for word, index in tok.word_index.items():
if index == yhat:
out_word = word
break
# append to input
in_text, result = out_word, result + ' ' + out_word
return result
Finalmente lo probamos para la palbra "haber" y encontramos el siguiente resultado.
In [21]:
seq=generate_seq(model,tok,'haber',5)
print(seq)
haber reído del sabor que te
Empecemos por cargar unas librerías que nos serán útiles.
In [0]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import math
Carguemos ahora el archivo .csv almacenado en el github del curso:
In [0]:
df = pd.read_csv('https://raw.githubusercontent.com/diplomado-bigdata-machinelearning-udea/Curso2/master/S10/Tweets.csv')
Demos un vistazo al dataframe de pandas que hemos creado:
In [24]:
df.head()
Out[24]:
tweet_id
airline_sentiment
airline_sentiment_confidence
negativereason
negativereason_confidence
airline
airline_sentiment_gold
name
negativereason_gold
retweet_count
text
tweet_coord
tweet_created
tweet_location
user_timezone
0
570306133677760513
neutral
1.0000
NaN
NaN
Virgin America
NaN
cairdin
NaN
0
@VirginAmerica What @dhepburn said.
NaN
2015-02-24 11:35:52 -0800
NaN
Eastern Time (US & Canada)
1
570301130888122368
positive
0.3486
NaN
0.0000
Virgin America
NaN
jnardino
NaN
0
@VirginAmerica plus you've added commercials t...
NaN
2015-02-24 11:15:59 -0800
NaN
Pacific Time (US & Canada)
2
570301083672813571
neutral
0.6837
NaN
NaN
Virgin America
NaN
yvonnalynn
NaN
0
@VirginAmerica I didn't today... Must mean I n...
NaN
2015-02-24 11:15:48 -0800
Lets Play
Central Time (US & Canada)
3
570301031407624196
negative
1.0000
Bad Flight
0.7033
Virgin America
NaN
jnardino
NaN
0
@VirginAmerica it's really aggressive to blast...
NaN
2015-02-24 11:15:36 -0800
NaN
Pacific Time (US & Canada)
4
570300817074462722
negative
1.0000
Can't Tell
1.0000
Virgin America
NaN
jnardino
NaN
0
@VirginAmerica and it's a really big bad thing...
NaN
2015-02-24 11:14:45 -0800
NaN
Pacific Time (US & Canada)
Correboremos la forma de la tabla con la función shape que hereda el dataframe df
In [25]:
df.shape
Out[25]:
(14640, 15)
Afortunadamente, quien realizó la tabla no rellenó espacio en los datos faltantes, por lo que la función read_csv los comprenderá como NaN. Veamos cuantas entradas en la tabla son NaN:
In [26]:
df.isnull().sum()
Out[26]:
tweet_id 0
airline_sentiment 0
airline_sentiment_confidence 0
negativereason 5462
negativereason_confidence 4118
airline 0
airline_sentiment_gold 14600
name 0
negativereason_gold 14608
retweet_count 0
text 0
tweet_coord 13621
tweet_created 0
tweet_location 4733
user_timezone 4820
dtype: int64
Por fortuna, nuevamente, las columnas text y airline_sentiment no tienen datos faltantes y estás entradas serán las variables independientes x e y, respectivamente. Visualicemos solamente estas columnas para comprender mejor el objetivo de nuestro modelo.
In [27]:
df[['text', 'airline_sentiment']].head()
Out[27]:
text
airline_sentiment
0
@VirginAmerica What @dhepburn said.
neutral
1
@VirginAmerica plus you've added commercials t...
positive
2
@VirginAmerica I didn't today... Must mean I n...
neutral
3
@VirginAmerica it's really aggressive to blast...
negative
4
@VirginAmerica and it's a really big bad thing...
negative
Veamos ahora cuantas instancias del dataset pertenecen a cada una de las tres clases:
In [28]:
df['airline_sentiment'].value_counts()
Out[28]:
negative 9178
neutral 3099
positive 2363
Name: airline_sentiment, dtype: int64
A los efectos de este problema, solo nos interesa el texto de cada tweet y su etiqueta de opinión. Por lo tanto, eliminaremos las características adicionales de nuestro conjunto de datos, por lo que solo tendremos 2 columnas al final.
Elegiremos no limpiar los tweets en sí mismos y no eliminar los identificadores de Twitter, los caracteres especiales y la puntuación, porque nuestra RNN aprenderá automáticamente estas variaciones y patrones.
Debemos transformar nuestros datos para la entrada en una RNN. Nuestro texto se codifica inicialmente como cadenas, por lo que debemos convertirlo en un vector de enteros. Idealmente, queremos que cada palabra única en nuestro conjunto de datos tenga su propia representación entera. Cada tweet se representaría como un vector con cada elemento que contiene un número entero que se puede asignar a una palabra específica. Esto se puede hacer usando la clase Keras Tokenizer. Dicha clase convierte un fragmento de texto en una secuencia de enteros con cada entero como el índice de un "token" en un diccionario. En general, la "tokenización" es el proceso de convertir oraciones en palabras. Encontramos que el tamaño del vocabulario de nuestro conjunto de datos es de 15,769 palabras.
Otro problema con nuestros datos es que cada tweet tiene una longitud diferente, lo que significa que nuestras longitudes de vector también no coinciden. Para corregir esto, primero encontramos el tweet más largo que tiene una longitud de 30 palabras. Luego podemos rellenar cada vector con 0 para que todos tengan las mismas dimensiones.
Convertimos nuestras etiquetas de sentimiento para que estén codificadas mediante un one-hot encoder. Esto significa que nuestra respuesta para cada tweet es ahora un vector de fila con 3 elementos que contendrá un 1 en la columna correspondiente a su etiqueta de opinión y 0 en el resto.
Crearemos un conjunto de entrenamiento y prueba utilizando una división 80:20. Nuestros datos de entrenamiento se reducirán aun más, en un 20%, para crear un conjunto de validación.
In [0]:
df['airline_sentiment'] = df['airline_sentiment'].replace('neutral', 1)
df['airline_sentiment'] = df['airline_sentiment'].replace('negative', 0)
df['airline_sentiment'] = df['airline_sentiment'].replace('positive', 2)
Selección de las instancias y las etiquetas:
In [0]:
X = df['text'] # data
y = df['airline_sentiment'] # labels
Importemos algunas librerías de keras para realizar el preprocesado de los datos:
In [0]:
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.text import text_to_word_sequence
Convirtamos los datos de entrenamiento en tensores para alimentarlos en la red neuronal.
In [0]:
t = Tokenizer()
t.fit_on_texts(X)
Determinemos el número de palabras únicas dentro de nuestros tweets:
In [33]:
vocab_size = len(t.word_index) + 1
print(vocab_size)
15769
Codifiquemos todo en enteros:
In [0]:
sequences = t.texts_to_sequences(X)
In [35]:
np.shape(sequences)
Out[35]:
(14640,)
Definamos una función para hallar el tweet, o la secuencia, de mayor longitud:
In [0]:
def max_tweet():
max_length = len(sequences[0])
for i in range(1, len(sequences)):
if len(sequences[i]) > max_length:
max_length = len(sequences[i])
return max_length
# Alternative (More elegant)
#tweet_num = max([len(x) for x in sequences])
In [42]:
tweet_num = max_tweet()
tweet_num
Out[42]:
36
Como cada reseña tiene un número diferente de palabras, realicemos un padding estableciendo cada vector como un vector de longitud tweet_num
In [0]:
from tensorflow.keras.preprocessing.sequence import pad_sequences
maxlen = tweet_num
padded_X = pad_sequences(sequences, padding='post', maxlen=maxlen)
In [47]:
# Make every tweet to have a lengt of 36 (max tweet)
padded_X.shape
Out[47]:
(14640, 36)
Convirtamos las etiquetas a variables categóricas:
In [0]:
labels = to_categorical(np.asarray(y))
Realicemos una división de los datos mediante la función de train_test_split de la librería de sklearn:
In [0]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(padded_X, labels, test_size = 0.2, random_state = 0)
Revicemos los tamaños de los datos de entrenamiento y evaluación:
In [52]:
print('X_train size:', X_train.shape)
print('y_train size:', y_train.shape)
print('X_test size:', X_test.shape)
print('y_test size:', y_test.shape)
X_train size: (11712, 36)
y_train size: (11712, 3)
X_test size: (2928, 36)
y_test size: (2928, 3)
GloVe corresponde a un "algoritmo de aprendizaje no supervisado para obtener representaciones vectoriales de palabras". Descargaremos datos del link y usaremos, especificamente, los enbeddings de 100 dimensiones de 400k palabras de la Wikipedia en inglés del 2014. Lo anterior se respresenta en un archivo txt que debemos analizar para crear un indexado que mapee las palabras a una representación vectorial.
Luego, debemos construir nuestra matriz de embedding, o matriz de contexto, de palabras como se discutió anteriormente. Las dimensiones de la matriz son nuestro tamaño de vocabulario por 100, que es nuestra dimensión de embedding como se especificó anteriormente. Cada fila de esta matriz es la representación vectorial GloVe de una palabra en nuestro vocabulario.
In [53]:
from google.colab import drive
drive.mount('/content/drive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly
Enter your authorization code:
··········
Mounted at /content/drive
In [59]:
cd drive/My Drive
/content/drive/My Drive
In [60]:
ls
Acercate/ DAPI/ Personal/
'Colab Notebooks'/ glove.6B.100d.txt TMP/
'Curso big data udea.pdf' Grafos/ 'universidad de antioquia'/
Carga total de embedding en memoria de una versión de dimensión 100:
In [63]:
embeddings_index = dict()
with open('glove.6B.100d.txt') as f:
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
print('Loaded %s word vectors.' % len(embeddings_index))
Loaded 400000 word vectors.
In [0]:
# encontrar relaciones similares entre palabras, por ejemplo rey es a reina como niño es a (esperamos que diga niña)
resta = embeddings_index["king"] - embeddings_index["queen"]
nuevo = embeddings_index["boy"] - resta
In [67]:
# encontrar el embeding que tiene una relacion similar entre rey y reina para niño
# el valor mas cercano se haya realizando un producto punto
maxn=0
palabra=""
for x in embeddings_index:
punto = np.dot(nuevo, embeddings_index[x])
punto/= np.sqrt(np.dot(nuevo,nuevo))
punto/=np.sqrt(np.dot(embeddings_index[x], embeddings_index[x]))
if punto>maxn:
maxn=punto
palabra=x
print(palabra)
boy
Dado que estamos usando GloVe, necesitamos crear una matriz de contexto embedding/palabra. Creemos una matriz de pesos para las palabras en el conjunto de entrenamiento y asignemos un embedding por cada palabra de este conjunto.
Obtengamos todas las palabras únicas en nuestro conjunto de entrenamiento (índice de tokenizado) y asocienmo el vector de pesos en el embedding GloVe.
In [0]:
# Definición del tamaño de la matriz embedding: Número de palabas únicas x dimensión del embedding (100)
embedding_matrix = np.zeros((vocab_size, 100))
# relleno de la matriz
for word, i in t.word_index.items(): # diccionario
embedding_vector = embeddings_index.get(word) # obtención de los vectores embedded de la palabra en GloVe.
if embedding_vector is not None:
# adición en la matriz
embedding_matrix[i] = embedding_vector # cada fila de la matriz.
Cargemos algunas librerias que nos resultará útiles a la hora de crear el modelo.
In [69]:
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import BatchNormalization
from keras.callbacks import EarlyStopping
Using TensorFlow backend.
La siguiente arquitectura de red comenzará con una capa embedding que puede considerarse como un diccionario que asigna índices enteros que representan palabras específicas para vectores densos. Utiliza estos enteros para encontrar su vector asociado en el embedding de palabras definidas por GloVe. La dimensión de entrada para esta capa corresponderá a nuestro tamaño de vocabulario y la salida es 100 para la dimensión de incrustación. Los pesos están definidos por la matriz embedding y fijaremos trainable=False, porque deseamos usar el embedding de palabras ya pre-entrenada. Queremos evitar actualizar los pesos pre-entrenados al entrenar el modelo. La longitud de entrada es 30, porque decidimos ajustar todas nuestras secuencias a este valor para garantizar que todos los vectores tuvieran la misma longitud.
In [0]:
keras.backend.clear_session()
# Creación de la capa embedding esando la matriz embedding predefinida.
from tensorflow.keras.layers import Embedding
# la entrada será vocab_size, y la salida 100
# para cargar los pesos de la matriz embedding hacemos trainable = False
embedding_layer = Embedding(input_dim=vocab_size, output_dim=100, weights=[embedding_matrix],
input_length = tweet_num, trainable=False)
Las entradas para el siguiente modelo propuesto son nuestros los datos de entrenamiento, que consisten en $11712$ con un $20\%$ usado como validación. Cada una de estas observaciones contiene 30 "características" que corresponden a cada palabra en el tweet. Cualquier 0 indica la ausencia de una palabra.
El modelo termina con una capa densa con 3 nodos, ya que se tienen 3 posibles etiquetas: positiva, neutral o negativa. Dado que codificamos nuestras etiquetas mediante one-hot encoder, utilizaremos una capa softmax para este problema de clasificación multiclase y obtener una probabilidad para cada clase. Además, usaremos la métrica de presición, ya que estamos tratando un problema de clasificación. Cuando usamos este método de predicción de Keras, obtendremos un vector de fila de 3 elementos para cada entrada. Cada elemento corresponde a una probabilidad de una de las 3 etiquetas. Por lo tanto, la etiqueta con la mayor probabilidad es el resultado predicho.
Compilaremos el modelo con el optimizador adam y una función de entropía cruzada categórica.
Crearemos para este problema de clasificación solo una capa LSTM, además de la capa embedding y softmax densa previamente especificadas. La dimensión de salida para la capa LSTM es 256, lo que significa que aumentaremos la dimensionalidad inicial de 100, que es la salida de la capa de inclusión. No parece existir una regla general para determinar el número de unidades en una capa RNN, por lo que inicialmente elegiremos 256 para aumentar la dimensionalidad y ver cómo funciona el modelo. También incluimos regularización con dropout y dropout recurrente. La primera es la tasa de deserción de las unidades de entrada de la capa, mientras que la segunda especifica la tasa de deserción de las unidades recurrentes. Dichos parámetros los configuraremos en 0.2 y 0.5 respectivamente. Entrenaremos este modelo durante 100 épocas con un tamaño de lote de 256 (PERO HACIENDO EARLY STOPPING).
In [71]:
lstm_mod1 = Sequential()
lstm_mod1.add(embedding_layer)
lstm_mod1.add(LSTM(256,
dropout = 0.2,
recurrent_dropout = 0.5))
lstm_mod1.add(Dense(3, activation='softmax'))
lstm_mod1.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
lstm_mod1.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 36, 100) 1576900
_________________________________________________________________
lstm (LSTM) (None, 256) 365568
_________________________________________________________________
dense (Dense) (None, 3) 771
=================================================================
Total params: 1,943,239
Trainable params: 366,339
Non-trainable params: 1,576,900
_________________________________________________________________
In [72]:
es=EarlyStopping(monitor='val_loss',patience=10)
hist_1 = lstm_mod1.fit(X_train, y_train,
validation_split = 0.2,
epochs=100, callbacks=[es],batch_size=256)
Train on 9369 samples, validate on 2343 samples
Epoch 1/100
9369/9369 [==============================] - 3s 319us/sample - loss: 0.8832 - acc: 0.6160 - val_loss: 0.8415 - val_acc: 0.6176
Epoch 2/100
9369/9369 [==============================] - 2s 249us/sample - loss: 0.8260 - acc: 0.6409 - val_loss: 0.7860 - val_acc: 0.6688
Epoch 3/100
9369/9369 [==============================] - 2s 248us/sample - loss: 0.7797 - acc: 0.6684 - val_loss: 0.7210 - val_acc: 0.6965
Epoch 4/100
9369/9369 [==============================] - 2s 253us/sample - loss: 0.7503 - acc: 0.6830 - val_loss: 0.7007 - val_acc: 0.7136
Epoch 5/100
9369/9369 [==============================] - 2s 255us/sample - loss: 0.7209 - acc: 0.7073 - val_loss: 0.6667 - val_acc: 0.7345
Epoch 6/100
9369/9369 [==============================] - 2s 249us/sample - loss: 0.7069 - acc: 0.7190 - val_loss: 0.6651 - val_acc: 0.7435
Epoch 7/100
9369/9369 [==============================] - 2s 245us/sample - loss: 0.6860 - acc: 0.7260 - val_loss: 0.6408 - val_acc: 0.7414
Epoch 8/100
9369/9369 [==============================] - 2s 247us/sample - loss: 0.6636 - acc: 0.7370 - val_loss: 0.6164 - val_acc: 0.7507
Epoch 9/100
9369/9369 [==============================] - 2s 244us/sample - loss: 0.6506 - acc: 0.7394 - val_loss: 0.5976 - val_acc: 0.7584
Epoch 10/100
9369/9369 [==============================] - 2s 245us/sample - loss: 0.6356 - acc: 0.7473 - val_loss: 0.5929 - val_acc: 0.7644
Epoch 11/100
9369/9369 [==============================] - 2s 243us/sample - loss: 0.6236 - acc: 0.7537 - val_loss: 0.5903 - val_acc: 0.7708
Epoch 12/100
9369/9369 [==============================] - 2s 243us/sample - loss: 0.6118 - acc: 0.7551 - val_loss: 0.5791 - val_acc: 0.7708
Epoch 13/100
9369/9369 [==============================] - 2s 241us/sample - loss: 0.6038 - acc: 0.7576 - val_loss: 0.5824 - val_acc: 0.7691
Epoch 14/100
9369/9369 [==============================] - 2s 245us/sample - loss: 0.5955 - acc: 0.7603 - val_loss: 0.5659 - val_acc: 0.7742
Epoch 15/100
9369/9369 [==============================] - 2s 242us/sample - loss: 0.5858 - acc: 0.7620 - val_loss: 0.5518 - val_acc: 0.7768
Epoch 16/100
9369/9369 [==============================] - 2s 247us/sample - loss: 0.5790 - acc: 0.7640 - val_loss: 0.5624 - val_acc: 0.7725
Epoch 17/100
9369/9369 [==============================] - 2s 243us/sample - loss: 0.5696 - acc: 0.7697 - val_loss: 0.5554 - val_acc: 0.7832
Epoch 18/100
9369/9369 [==============================] - 2s 243us/sample - loss: 0.5579 - acc: 0.7766 - val_loss: 0.5521 - val_acc: 0.7892
Epoch 19/100
9369/9369 [==============================] - 2s 249us/sample - loss: 0.5524 - acc: 0.7775 - val_loss: 0.5404 - val_acc: 0.7810
Epoch 20/100
9369/9369 [==============================] - 2s 244us/sample - loss: 0.5471 - acc: 0.7784 - val_loss: 0.5359 - val_acc: 0.7798
Epoch 21/100
9369/9369 [==============================] - 2s 247us/sample - loss: 0.5417 - acc: 0.7812 - val_loss: 0.5347 - val_acc: 0.7875
Epoch 22/100
9369/9369 [==============================] - 2s 259us/sample - loss: 0.5302 - acc: 0.7870 - val_loss: 0.5290 - val_acc: 0.7904
Epoch 23/100
9369/9369 [==============================] - 2s 251us/sample - loss: 0.5328 - acc: 0.7826 - val_loss: 0.5215 - val_acc: 0.7994
Epoch 24/100
9369/9369 [==============================] - 2s 250us/sample - loss: 0.5280 - acc: 0.7838 - val_loss: 0.5234 - val_acc: 0.7909
Epoch 25/100
9369/9369 [==============================] - 2s 250us/sample - loss: 0.5162 - acc: 0.7921 - val_loss: 0.5273 - val_acc: 0.7990
Epoch 26/100
9369/9369 [==============================] - 2s 260us/sample - loss: 0.5111 - acc: 0.7897 - val_loss: 0.5239 - val_acc: 0.7998
Epoch 27/100
9369/9369 [==============================] - 2s 250us/sample - loss: 0.5090 - acc: 0.7960 - val_loss: 0.5308 - val_acc: 0.7930
Epoch 28/100
9369/9369 [==============================] - 2s 242us/sample - loss: 0.5021 - acc: 0.7954 - val_loss: 0.5228 - val_acc: 0.7892
Epoch 29/100
9369/9369 [==============================] - 2s 246us/sample - loss: 0.4930 - acc: 0.7981 - val_loss: 0.5116 - val_acc: 0.7990
Epoch 30/100
9369/9369 [==============================] - 2s 262us/sample - loss: 0.4965 - acc: 0.7973 - val_loss: 0.5148 - val_acc: 0.7977
Epoch 31/100
9369/9369 [==============================] - 2s 244us/sample - loss: 0.4859 - acc: 0.8016 - val_loss: 0.5113 - val_acc: 0.7968
Epoch 32/100
9369/9369 [==============================] - 2s 251us/sample - loss: 0.4751 - acc: 0.8104 - val_loss: 0.5115 - val_acc: 0.7968
Epoch 33/100
9369/9369 [==============================] - 2s 249us/sample - loss: 0.4768 - acc: 0.8100 - val_loss: 0.5178 - val_acc: 0.7977
Epoch 34/100
9369/9369 [==============================] - 2s 249us/sample - loss: 0.4666 - acc: 0.8113 - val_loss: 0.5165 - val_acc: 0.8011
Epoch 35/100
9369/9369 [==============================] - 2s 248us/sample - loss: 0.4642 - acc: 0.8151 - val_loss: 0.5143 - val_acc: 0.7968
Epoch 36/100
9369/9369 [==============================] - 2s 251us/sample - loss: 0.4497 - acc: 0.8218 - val_loss: 0.5242 - val_acc: 0.7964
Epoch 37/100
9369/9369 [==============================] - 2s 248us/sample - loss: 0.4593 - acc: 0.8199 - val_loss: 0.5088 - val_acc: 0.8015
Epoch 38/100
9369/9369 [==============================] - 2s 253us/sample - loss: 0.4525 - acc: 0.8184 - val_loss: 0.5096 - val_acc: 0.8015
Epoch 39/100
9369/9369 [==============================] - 2s 251us/sample - loss: 0.4517 - acc: 0.8196 - val_loss: 0.5069 - val_acc: 0.8011
Epoch 40/100
9369/9369 [==============================] - 2s 247us/sample - loss: 0.4384 - acc: 0.8252 - val_loss: 0.5003 - val_acc: 0.8041
Epoch 41/100
9369/9369 [==============================] - 2s 247us/sample - loss: 0.4357 - acc: 0.8278 - val_loss: 0.5006 - val_acc: 0.8037
Epoch 42/100
9369/9369 [==============================] - 2s 255us/sample - loss: 0.4325 - acc: 0.8284 - val_loss: 0.5095 - val_acc: 0.8020
Epoch 43/100
9369/9369 [==============================] - 2s 243us/sample - loss: 0.4311 - acc: 0.8318 - val_loss: 0.5188 - val_acc: 0.8041
Epoch 44/100
9369/9369 [==============================] - 2s 252us/sample - loss: 0.4225 - acc: 0.8299 - val_loss: 0.5620 - val_acc: 0.8037
Epoch 45/100
9369/9369 [==============================] - 2s 255us/sample - loss: 0.4223 - acc: 0.8331 - val_loss: 0.5032 - val_acc: 0.8071
Epoch 46/100
9369/9369 [==============================] - 2s 256us/sample - loss: 0.4141 - acc: 0.8367 - val_loss: 0.4998 - val_acc: 0.8045
Epoch 47/100
9369/9369 [==============================] - 2s 257us/sample - loss: 0.4044 - acc: 0.8401 - val_loss: 0.5017 - val_acc: 0.8075
Epoch 48/100
9369/9369 [==============================] - 2s 253us/sample - loss: 0.4021 - acc: 0.8427 - val_loss: 0.5066 - val_acc: 0.8011
Epoch 49/100
9369/9369 [==============================] - 2s 244us/sample - loss: 0.4024 - acc: 0.8408 - val_loss: 0.5000 - val_acc: 0.8092
Epoch 50/100
9369/9369 [==============================] - 2s 249us/sample - loss: 0.3929 - acc: 0.8464 - val_loss: 0.5171 - val_acc: 0.8101
Epoch 51/100
9369/9369 [==============================] - 2s 252us/sample - loss: 0.3967 - acc: 0.8414 - val_loss: 0.4991 - val_acc: 0.8084
Epoch 52/100
9369/9369 [==============================] - 2s 257us/sample - loss: 0.3929 - acc: 0.8445 - val_loss: 0.5124 - val_acc: 0.8037
Epoch 53/100
9369/9369 [==============================] - 2s 254us/sample - loss: 0.3836 - acc: 0.8492 - val_loss: 0.5222 - val_acc: 0.8041
Epoch 54/100
9369/9369 [==============================] - 2s 245us/sample - loss: 0.3733 - acc: 0.8537 - val_loss: 0.5145 - val_acc: 0.8092
Epoch 55/100
9369/9369 [==============================] - 2s 250us/sample - loss: 0.3758 - acc: 0.8501 - val_loss: 0.5304 - val_acc: 0.8041
Epoch 56/100
9369/9369 [==============================] - 2s 257us/sample - loss: 0.3766 - acc: 0.8548 - val_loss: 0.5112 - val_acc: 0.8105
Epoch 57/100
9369/9369 [==============================] - 2s 250us/sample - loss: 0.3610 - acc: 0.8605 - val_loss: 0.5090 - val_acc: 0.8105
Epoch 58/100
9369/9369 [==============================] - 2s 254us/sample - loss: 0.3574 - acc: 0.8606 - val_loss: 0.5256 - val_acc: 0.8084
Epoch 59/100
9369/9369 [==============================] - 2s 251us/sample - loss: 0.3572 - acc: 0.8599 - val_loss: 0.5127 - val_acc: 0.8122
Epoch 60/100
9369/9369 [==============================] - 2s 250us/sample - loss: 0.3521 - acc: 0.8639 - val_loss: 0.5266 - val_acc: 0.8105
Epoch 61/100
9369/9369 [==============================] - 2s 253us/sample - loss: 0.3455 - acc: 0.8684 - val_loss: 0.5207 - val_acc: 0.8109
Determinemos la precisión en el entrenamiento y la evaluación:
In [73]:
loss, accuracy = lstm_mod1.evaluate(X_train, y_train, verbose=False)
print("Training Accuracy: {:.4f}".format(accuracy))
loss, accuracy = lstm_mod1.evaluate(X_test, y_test, verbose=False)
print("Testing Accuracy: {:.4f}".format(accuracy))
Training Accuracy: 0.8888
Testing Accuracy: 0.7999
In [74]:
acc = hist_1.history['acc']
val_acc = hist_1.history['val_acc']
loss = hist_1.history['loss']
val_loss = hist_1.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'g', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'g', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
A su vez, podemos visualizar nuestros resultados mediante una matriz de confusión. Importemos algunas librerías.
In [0]:
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
In [0]:
y_pred = lstm_mod1.predict(X_test) # salida de las probabilidades de cada sentimiento
# Creación de un arrat de numpy vacio que sea igual a la longitud de las observaciones de entrenamiento
y_pred_array = np.zeros(X_test.shape[0])
# Determinación de la clase con mayor probabilidada
for i in range(0, y_pred.shape[0]):
label_predict = np.argmax(y_pred[i]) # columna de probabilidades máximas
y_pred_array[i] = label_predict
# Conversión a enteros.
y_pred_array = y_pred_array.astype(int)
In [0]:
# Conversión de y_test a un array 1d de numpy:
y_test_array = np.zeros(X_test.shape[0])
# Determinaciónd e la clase con 1
for i in range(0, y_test.shape[0]):
label_predict = np.argmax(y_test[i])
y_test_array[i] = label_predict
y_test_array = y_test_array.astype(int)
In [0]:
class_names = np.array(['Negative', 'Neutral', 'Positive'])
In [79]:
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
classes = classes[unique_labels(y_true, y_pred)]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax
np.set_printoptions(precision=2)
# Plot non-normalized confusion matrix
plot_confusion_matrix(y_test_array, y_pred_array, classes=class_names,
title='Confusion matrix, without normalization')
# Plot normalized confusion matrix
plot_confusion_matrix(y_test_array, y_pred_array, classes=class_names, normalize=True,
title='Normalized confusion matrix')
plt.show()
Confusion matrix, without normalization
[[1654 136 80]
[ 189 366 59]
[ 67 55 322]]
Normalized confusion matrix
[[0.88 0.07 0.04]
[0.31 0.6 0.1 ]
[0.15 0.12 0.73]]
Las redes LSTM y GRU resuelven la inestabilidad de gradientes ya que proveen diferentes rutas por las que se puede propagar el gradiente. En cierta forma son una autopista de información. Sin embargo, estas redes todavía tienen un problema de memoria que las arquitecturas LSTM/GRU simplemente alivianan. Este problema se debe a que estamos intentando condensar toda la información de todos los pasos temporales en un sólo vector de estado. Adicionalmente, las redes neuronales no son paralelizables lo que hace que el tiempo de entrenamiento e inferencia sea considerablemente más lento que las redes convolucionales. Este último inconveniente lo resolveremos más adelante mediante las redes Transformer.
Para resolver el problema de la memoria vamos a ver el mecanismo de atención de Bahdanau. Por lo general este mecanismo se aplica para redes neuronales recurrentes encoder/decoder. Sin atención, la arquitectura encoder/decoder tiene la siguiente forma:
Bajo este mecanismo, no debemos alimentar el decoder únicamente con los estados anteriores sino que se hace una combinación lineal de todos los estados del encoder. Esta combinación lineal se conoce como vector de contexto. Para calcular los coeficientes de la combinación lineal se usa una red neuronal aparte, como se ve en la siguiente figura (que muestra la tecnica de atencion de Luong) Esta tecnica se repite por cada lstm del decoder con todos los datos del encoder:
El proceso se resume en los siguientes pasos:
La atención de Luong hace este mismo proceso pero con las salidas del decoder
Cargamos los datos. Son pares de oraciones en inglés/español separados por tabs
In [80]:
!wget http://www.manythings.org/anki/spa-eng.zip
!unzip -o spa-eng.zip
--2020-02-15 15:36:34-- http://www.manythings.org/anki/spa-eng.zip
Resolving www.manythings.org (www.manythings.org)... 104.24.108.196, 104.24.109.196, 2606:4700:3033::6818:6dc4, ...
Connecting to www.manythings.org (www.manythings.org)|104.24.108.196|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4767708 (4.5M) [application/zip]
Saving to: ‘spa-eng.zip’
spa-eng.zip 100%[===================>] 4.55M 13.9MB/s in 0.3s
2020-02-15 15:36:35 (13.9 MB/s) - ‘spa-eng.zip’ saved [4767708/4767708]
Archive: spa-eng.zip
inflating: _about.txt
inflating: spa.txt
In [84]:
import re
import numpy as np
import matplotlib.pyplot as plt
def quitarchar(texto):
abc=' abcdefghijklmnopqrstuvwxyzáéíóúüñ.,!?0123456789'
texto=re.sub(r"([?.!,¿])", r" \1 ", texto)
texto=re.sub(r'[" "]+', " ", texto)
nuevotexto=''
for x in texto.lower():
if x in abc: nuevotexto+=x
nuevotexto = re.sub(r"[^a-záéíóúüñ?.!,¿]+", " ", nuevotexto)
return nuevotexto.strip()
with open('spa.txt','r',encoding='utf8') as f:
datos=[(quitarchar(x.split('\t')[0]),quitarchar(x.split('\t')[1])) for x in f.readlines()]
datos[:10]
Out[84]:
[('go .', 've .'),
('go .', 'vete .'),
('go .', 'vaya .'),
('go .', 'váyase .'),
('hi .', 'hola .'),
('run !', 'corre !'),
('run !', 'corran !'),
('run !', 'corra !'),
('run !', 'corred !'),
('run .', 'corred .')]
Se cargan los datos en inglés como inputs y en español como outputs
In [85]:
inputs=['<start> '+x[0]+' <end>' for x in datos]
outputs=['<start> '+x[1]+' <end>' for x in datos]
maxin=max([len(x.split(' ')) for x in inputs])
maxout=max([len(x.split(' ')) for x in outputs])
len(inputs),len(outputs),maxin,maxout
Out[85]:
(123335, 123335, 53, 59)
In [86]:
inputs[150],outputs[150]
Out[86]:
('<start> go away . <end>', '<start> lárguese . <end>')
Se hace una lista de todas las palabras en inglés y en español
In [0]:
wordsin=['-']
wordsout=['-']
for x in inputs:
for y in x.split(' '):
if y not in wordsin:wordsin.append(y)
for x in outputs:
for y in x.split(' '):
if y not in wordsout:wordsout.append(y)
Hay que calcular la longitud de oraciones más largas y la cantidad de palabras únicas en cada idioma
In [88]:
cantin=len(wordsin)
cantout=len(wordsout)
cantin,cantout
Out[88]:
(13474, 26212)
In [0]:
dictin={x:i for i,x in enumerate(wordsin)}
dictout={x:i for i,x in enumerate(wordsout)}
Debemos tokenizar por batches convirtiendo cada caracter a una secuencia de enteros
In [0]:
def tokenbatch(bsize,shuffle=True):
while 1:
indices=np.array(range(len(inputs)))
if shuffle: np.random.shuffle(indices)
for i in range(len(inputs)//bsize):
batchidx=indices[i*bsize:(i+1)*bsize]
encin=np.zeros([bsize,maxin])
decin=np.zeros([bsize,maxout])
decout=np.zeros([bsize,maxout])
for j in range(bsize):
for t,letra in enumerate(inputs[batchidx[j]].split(' ')):
encin[j,t]=dictin[letra]
for t,letra in enumerate(outputs[batchidx[j]].split(' ')):
decin[j,t]=dictout[letra]
if t>0:
decout[j,t-1]=dictout[letra]
yield (encin[:,::-1],decin),decout
Tomemos un batch de ejemplo
In [91]:
(encin,decin),decout=next(tokenbatch(2))
encin.shape,decin.shape,decout.shape
Out[91]:
((2, 53), (2, 59), (2, 59))
In [92]:
%tensorflow_version 2.x
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input,TimeDistributed,Dense,GRU,Concatenate,Attention,Embedding
bsize=32
hsize=256
esize=256
TensorFlow is already loaded. Please restart the runtime to change versions.
Hay que configurar las variables de entrada al modelo
In [0]:
encoder_in = Input(batch_shape=(bsize, maxin))
decoder_in = Input(batch_shape=(bsize, maxout))
Encoder
In [0]:
# Tengo una cantidad de palabras = cantin pero quiero un vector de tamaño = esize en ingles
encoder_embed = Embedding(cantin,esize)
encoder_embeddings = encoder_embed(encoder_in)
encoder_gru = GRU(hsize, return_sequences=True, return_state=True)
encoder_out, encoder_state = encoder_gru(encoder_embeddings)
Decoder
In [0]:
# Tengo una cantidad de palabras = cantout pero quiero un vector de tamaño = esize en español
decoder_embed = Embedding(cantout,esize)
decoder_embeddings = decoder_embed(decoder_in)
decoder_gru = GRU(hsize, return_sequences=True, return_state=True)
decoder_out, decoder_state = decoder_gru(decoder_embeddings, initial_state=encoder_state)
Atención
In [0]:
attn_layer = Attention()
attn_out = attn_layer([decoder_out,encoder_out])
In [97]:
attn_out.shape
Out[97]:
TensorShape([Dimension(32), Dimension(59), Dimension(256)])
Output
In [0]:
decoder_concat_input = Concatenate(axis=-1)([decoder_out, attn_out])
dense = Dense(cantout, activation='softmax')
dense_time = TimeDistributed(dense)
decoder_pred = dense_time(decoder_concat_input)
Entrenamiento
In [101]:
# loss='sparse_categorical_crossentropy' la parte de SPARSE es para evitar el one hot encoding
# ya que seria un vector de 26000 filas donde 25999 serian 0, eso por cada palabra, y gasta mucha ram,
# en lugar de eso se usa el indice de la palabra (ie. 73)
modelo = Model(inputs=[encoder_in, decoder_in], outputs=decoder_pred)
modelo.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
modelo.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(32, 53)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(32, 59)] 0
__________________________________________________________________________________________________
embedding_1 (Embedding) (32, 53, 256) 3449344 input_1[0][0]
__________________________________________________________________________________________________
embedding_2 (Embedding) (32, 59, 256) 6710272 input_2[0][0]
__________________________________________________________________________________________________
gru (GRU) [(32, 53, 256), (32, 393984 embedding_1[0][0]
__________________________________________________________________________________________________
gru_1 (GRU) [(32, 59, 256), (32, 393984 embedding_2[0][0]
gru[0][1]
__________________________________________________________________________________________________
attention (Attention) (32, 59, 256) 0 gru_1[0][0]
gru[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (32, 59, 512) 0 gru_1[0][0]
attention[0][0]
__________________________________________________________________________________________________
time_distributed (TimeDistribut (32, 59, 26212) 13446756 concatenate[0][0]
==================================================================================================
Total params: 24,394,340
Trainable params: 24,394,340
Non-trainable params: 0
__________________________________________________________________________________________________
In [102]:
modelo.fit(x=tokenbatch(bsize),epochs=1,steps_per_epoch=len(inputs)//bsize)
3854/3854 [==============================] - 697s 181ms/step - loss: 0.5918
Out[102]:
<tensorflow.python.keras.callbacks.History at 0x7f6319c93470>
Entradas
In [0]:
bsize = 1
encoder_inf_inputs = Input(batch_shape=(bsize, maxin))
decoder_inf_inputs = Input(batch_shape=(bsize, 1))
encoder_inf_states = Input(batch_shape=(bsize, maxin, hsize))
decoder_init_state = Input(batch_shape=(bsize, hsize))
Encoder
In [0]:
encoder_inf_embeddings = encoder_embed(encoder_inf_inputs)
encoder_inf_out, encoder_inf_state = encoder_gru(encoder_inf_embeddings)
encoder_model = Model(inputs=encoder_inf_inputs, outputs=[encoder_inf_out, encoder_inf_state])
Decoder
In [0]:
decoder_inf_embeddings = decoder_embed(decoder_inf_inputs)
decoder_inf_out, decoder_inf_state = decoder_gru(decoder_inf_embeddings, initial_state=decoder_init_state)
attn_inf_out = attn_layer([decoder_inf_out,encoder_inf_states])
attn_inf_states= attn_layer._calculate_scores(decoder_inf_out,encoder_inf_states)
decoder_inf_concat = Concatenate(axis=-1)([decoder_inf_out, attn_inf_out])
decoder_inf_pred = TimeDistributed(dense)(decoder_inf_concat)
decoder_model = Model(inputs=[encoder_inf_states, decoder_init_state, decoder_inf_inputs],outputs=[decoder_inf_pred, attn_inf_states, decoder_inf_state])
Token encoding de la frase a traducir
In [119]:
frase=inputs[454]
seqin=np.zeros((1,maxin))
for t,word in enumerate(frase.split(' ')):
seqin[0,t]=dictin[word]
seqin=seqin[:,::-1]
frase,seqin.shape
Out[119]:
('<start> tom fell . <end>', (1, 53))
Inicializar la frase traducida
In [0]:
seqout=np.zeros((1,1))
seqout[0,0]=dictout['<start>']
In [121]:
enc_out, enc_ult = encoder_model.predict(seqin)
enc_out.shape , enc_ult.shape
Out[121]:
((1, 53, 256), (1, 256))
In [122]:
dec_state = enc_ult
acumatt = []
texto = ''
for i in range(100):
dec_out, attention, dec_state = decoder_model.predict([enc_out, dec_state, seqout])
dec_ind = np.argmax(dec_out, axis=-1)[0, 0]
if dec_ind == dictout['<end>']:
break
seqout=np.zeros((1,1))
seqout[0,0]=dec_ind
acumatt.append(attention[:,:,::-1])
texto+=' '
texto+=wordsout[dec_ind]
texto=texto.strip()
print(texto)
tom se quedó dormido .
La atención además nos provee una herramienta de visualización bastante intuitiva conocida como la alineación. Esta consta en graficar los puntajes de la atención para cada paso del decoder. Esto nos dirá en cada estado del decoder a que paso en el encoder se le prestó más atención.
In [116]:
atencion=np.array(acumatt)
atencion=atencion[:,:,:,:len(frase.split(' '))]
atencion.shape=(-1,len(frase.split(' ')))
atencion.shape
Out[116]:
(4, 6)
In [0]:
atencion=np.exp(atencion)/np.sum(np.exp(atencion),axis=-1).reshape(-1,1)
In [118]:
fig, ax = plt.subplots(figsize=(10,10))
ax.set_xticks(np.arange(len(frase.split(' '))))
ax.set_yticks(np.arange(len(texto.split(' '))))
ax.set_xticklabels(list(frase.split(' ')))
ax.set_yticklabels(list(texto.split(' ')))
im = ax.imshow(atencion)
Hasta el momento, la atención nos ha permitido calcular la relación entre dos posiciones en dos secuencias diferentes. La auto-atención implica calcular la relación de dos posiciones en la misma secuencia. Para cada entrada se debe calcular un query, un key y un value usando una red densa. Estos son los vectores que se usan para la atención tomando el query como si fuera el estado del decoder, el key se usa para calcular los puntajes y el value para calcular el vector de contexto.
La red neuronal transformer se introdujo en el artículo "Attention is all you need" de Google que intenta reemplazar las redes recurrentes con mecanismos de atención unicamente (De ahí sale el nombre). Para lograrlo se usa auto-atención en el encoder y decoder y atención normal entre ellos de la siguiente manera:
En el segundo paso se debe usar una máscara ya que en la inferencia no se sabrá cuales son las salidas que siguen. Esto permite paralelizar las operaciones en el encoder y el decoder sin necesidad de la conexión recurrente.
In [0]:
In [0]:
%tensorflow_version 2.x
TensorFlow is already loaded. Please restart the runtime to change versions.
El problema de la predicción de series de tiempo es algo más complicado que simplemente hacer una regresión, esto ya que se agrega la complejidad de la dependencia de la secuencia (de los valores anteriormente predichos).
Es en estos casos que las redes con memoria son muy útiles.
Haremos a continuación un par de ejemplos de predicción simple, intentaremos predecir el numero de pasajeros que viajarán en avión en aerolineas internacionales, més a mes (en unidades de 1000). Usaremos para ello un dataset que inicia en enero de 1949 y va hasta diciembre de 1960, es decir 144 observaciones.
Descarguemos los datos y visualicemos.
In [123]:
!wget https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv
--2020-02-15 16:25:47-- https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2180 (2.1K) [text/plain]
Saving to: ‘airline-passengers.csv’
airline-passengers. 100%[===================>] 2.13K --.-KB/s in 0s
2020-02-15 16:25:47 (4.24 MB/s) - ‘airline-passengers.csv’ saved [2180/2180]
In [124]:
import pandas
import matplotlib.pyplot as plt
dataset = pandas.read_csv('airline-passengers.csv', usecols=[1])
dataset = dataset.astype('float32')
plt.plot(dataset)
plt.ylabel('Passengers in units of 1000')
plt.xlabel('Months from 01-1949')
plt.show()
In [125]:
dataset.head()
Out[125]:
Passengers
0
112.0
1
118.0
2
132.0
3
129.0
4
121.0
In [0]:
import numpy
import math
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
In [0]:
# fijemos una semilla para que nuestros resultados sean reproducibles
numpy.random.seed(7)
In [0]:
# usemos scikitlearn para normalizar los datos
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
Procederemos a separar nuestros datos en el conjunto de entrenamiento y test, un detalle importante a tener en cuenta en éste tipo de tareas es el ordenamiento temporar.
Si hacemos un particionado de manera típica, los datos serán mezclados de manera aleatoria antes de su partición, esto hará que se pierda el ordenamiento y por tanto que sea imposible capturar las relaciones temporales en los datos.
In [129]:
# split into train and test sets
train_size = int(len(dataset) * 0.65)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
print(len(train), len(test))
93 51
Con la siguiente función crearemos una matriz cuyas columnas iran generando un delay respecto a la anterior hasta llegar a un desface del tamaño del look back.
Con esto crearemos el conjuto de datos adecuado para ingresar a nuestra red de la forma.
$x(0),...,x(t),x(t+1),...,x(LB)$
In [0]:
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return numpy.array(dataX), numpy.array(dataY)
In [131]:
# en este caso X=t, Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
print(trainX[:10].T)
print(trainY.T[:10])
[[0.02 0.03 0.05 0.05 0.03 0.06 0.08 0.08 0.06 0.03]]
[0.03 0.05 0.05 0.03 0.06 0.08 0.08 0.06 0.03 0. ]
In [0]:
# Hagamos un reshape de la forma [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
A esta forma de crear los datos la llamamos ventana, es decir, abrimos una ventana del tamaño del look_back y la red irá viendo los datos poco a poco a medida que la ventana va recorriendo la señal.
Creemos ahora una pequeña red LSTM con 4 celdas y un shape [1, look_back], es decir a la red le entrará un vector del tamaño de la ventana.
In [133]:
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
Train on 91 samples
Epoch 1/100
91/91 - 1s - loss: 0.0615
Epoch 2/100
91/91 - 0s - loss: 0.0333
Epoch 3/100
91/91 - 0s - loss: 0.0245
Epoch 4/100
91/91 - 0s - loss: 0.0218
Epoch 5/100
91/91 - 0s - loss: 0.0208
Epoch 6/100
91/91 - 0s - loss: 0.0200
Epoch 7/100
91/91 - 0s - loss: 0.0194
Epoch 8/100
91/91 - 0s - loss: 0.0184
Epoch 9/100
91/91 - 0s - loss: 0.0177
Epoch 10/100
91/91 - 0s - loss: 0.0169
Epoch 11/100
91/91 - 0s - loss: 0.0162
Epoch 12/100
91/91 - 0s - loss: 0.0155
Epoch 13/100
91/91 - 0s - loss: 0.0149
Epoch 14/100
91/91 - 0s - loss: 0.0140
Epoch 15/100
91/91 - 0s - loss: 0.0133
Epoch 16/100
91/91 - 0s - loss: 0.0124
Epoch 17/100
91/91 - 0s - loss: 0.0116
Epoch 18/100
91/91 - 0s - loss: 0.0108
Epoch 19/100
91/91 - 0s - loss: 0.0100
Epoch 20/100
91/91 - 0s - loss: 0.0092
Epoch 21/100
91/91 - 0s - loss: 0.0084
Epoch 22/100
91/91 - 0s - loss: 0.0076
Epoch 23/100
91/91 - 0s - loss: 0.0069
Epoch 24/100
91/91 - 0s - loss: 0.0061
Epoch 25/100
91/91 - 0s - loss: 0.0054
Epoch 26/100
91/91 - 0s - loss: 0.0048
Epoch 27/100
91/91 - 0s - loss: 0.0042
Epoch 28/100
91/91 - 0s - loss: 0.0038
Epoch 29/100
91/91 - 0s - loss: 0.0034
Epoch 30/100
91/91 - 0s - loss: 0.0030
Epoch 31/100
91/91 - 0s - loss: 0.0027
Epoch 32/100
91/91 - 0s - loss: 0.0025
Epoch 33/100
91/91 - 0s - loss: 0.0023
Epoch 34/100
91/91 - 0s - loss: 0.0022
Epoch 35/100
91/91 - 0s - loss: 0.0021
Epoch 36/100
91/91 - 0s - loss: 0.0020
Epoch 37/100
91/91 - 0s - loss: 0.0020
Epoch 38/100
91/91 - 0s - loss: 0.0019
Epoch 39/100
91/91 - 0s - loss: 0.0019
Epoch 40/100
91/91 - 0s - loss: 0.0019
Epoch 41/100
91/91 - 0s - loss: 0.0019
Epoch 42/100
91/91 - 0s - loss: 0.0019
Epoch 43/100
91/91 - 0s - loss: 0.0019
Epoch 44/100
91/91 - 0s - loss: 0.0019
Epoch 45/100
91/91 - 0s - loss: 0.0019
Epoch 46/100
91/91 - 0s - loss: 0.0019
Epoch 47/100
91/91 - 0s - loss: 0.0019
Epoch 48/100
91/91 - 0s - loss: 0.0019
Epoch 49/100
91/91 - 0s - loss: 0.0019
Epoch 50/100
91/91 - 0s - loss: 0.0018
Epoch 51/100
91/91 - 0s - loss: 0.0019
Epoch 52/100
91/91 - 0s - loss: 0.0019
Epoch 53/100
91/91 - 0s - loss: 0.0019
Epoch 54/100
91/91 - 0s - loss: 0.0019
Epoch 55/100
91/91 - 0s - loss: 0.0019
Epoch 56/100
91/91 - 0s - loss: 0.0019
Epoch 57/100
91/91 - 0s - loss: 0.0019
Epoch 58/100
91/91 - 0s - loss: 0.0018
Epoch 59/100
91/91 - 0s - loss: 0.0019
Epoch 60/100
91/91 - 0s - loss: 0.0019
Epoch 61/100
91/91 - 0s - loss: 0.0018
Epoch 62/100
91/91 - 0s - loss: 0.0019
Epoch 63/100
91/91 - 0s - loss: 0.0019
Epoch 64/100
91/91 - 0s - loss: 0.0019
Epoch 65/100
91/91 - 0s - loss: 0.0019
Epoch 66/100
91/91 - 0s - loss: 0.0019
Epoch 67/100
91/91 - 0s - loss: 0.0019
Epoch 68/100
91/91 - 0s - loss: 0.0018
Epoch 69/100
91/91 - 0s - loss: 0.0019
Epoch 70/100
91/91 - 0s - loss: 0.0019
Epoch 71/100
91/91 - 0s - loss: 0.0018
Epoch 72/100
91/91 - 0s - loss: 0.0019
Epoch 73/100
91/91 - 0s - loss: 0.0019
Epoch 74/100
91/91 - 0s - loss: 0.0019
Epoch 75/100
91/91 - 0s - loss: 0.0019
Epoch 76/100
91/91 - 0s - loss: 0.0019
Epoch 77/100
91/91 - 0s - loss: 0.0018
Epoch 78/100
91/91 - 0s - loss: 0.0019
Epoch 79/100
91/91 - 0s - loss: 0.0019
Epoch 80/100
91/91 - 0s - loss: 0.0019
Epoch 81/100
91/91 - 0s - loss: 0.0019
Epoch 82/100
91/91 - 0s - loss: 0.0019
Epoch 83/100
91/91 - 0s - loss: 0.0018
Epoch 84/100
91/91 - 0s - loss: 0.0019
Epoch 85/100
91/91 - 0s - loss: 0.0018
Epoch 86/100
91/91 - 0s - loss: 0.0018
Epoch 87/100
91/91 - 0s - loss: 0.0019
Epoch 88/100
91/91 - 0s - loss: 0.0019
Epoch 89/100
91/91 - 0s - loss: 0.0019
Epoch 90/100
91/91 - 0s - loss: 0.0019
Epoch 91/100
91/91 - 0s - loss: 0.0019
Epoch 92/100
91/91 - 0s - loss: 0.0019
Epoch 93/100
91/91 - 0s - loss: 0.0019
Epoch 94/100
91/91 - 0s - loss: 0.0019
Epoch 95/100
91/91 - 0s - loss: 0.0019
Epoch 96/100
91/91 - 0s - loss: 0.0018
Epoch 97/100
91/91 - 0s - loss: 0.0019
Epoch 98/100
91/91 - 0s - loss: 0.0018
Epoch 99/100
91/91 - 0s - loss: 0.0019
Epoch 100/100
91/91 - 0s - loss: 0.0019
Out[133]:
<tensorflow.python.keras.callbacks.History at 0x7f63192f7320>
Con nuestro modelo a mano hagamos una predicción, note que la predicción se hace sobre el conjunto de test (testX) completo, no uno a uno. Además recuerde invertir el escalamiento de los datos, si no los resultados que optenga no concordarán con los deseados.
In [135]:
#Predicción
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# desescalemos la predicción
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculemos nuestro error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
Train Score: 22.06 RMSE
Test Score: 48.36 RMSE
In [136]:
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
#se hace un corrimiento de los datos predichos para poder visualizarlos
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
Recuerde, cuando hablamos de la ventana es el look_back el que la define, por ejemplo podemos usar un tamaño mayo a 1, veamos.
In [137]:
look_back = 3 #ventana de 3 pasos de tiempo
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# reshape
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
# Creamos y entrenemos el modelo
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
# Predicción
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# desescalado
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculo del error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
# graficado
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
Train on 89 samples
Epoch 1/100
89/89 - 1s - loss: 0.0186
Epoch 2/100
89/89 - 0s - loss: 0.0072
Epoch 3/100
89/89 - 0s - loss: 0.0059
Epoch 4/100
89/89 - 0s - loss: 0.0050
Epoch 5/100
89/89 - 0s - loss: 0.0043
Epoch 6/100
89/89 - 0s - loss: 0.0037
Epoch 7/100
89/89 - 0s - loss: 0.0033
Epoch 8/100
89/89 - 0s - loss: 0.0029
Epoch 9/100
89/89 - 0s - loss: 0.0027
Epoch 10/100
89/89 - 0s - loss: 0.0025
Epoch 11/100
89/89 - 0s - loss: 0.0024
Epoch 12/100
89/89 - 0s - loss: 0.0024
Epoch 13/100
89/89 - 0s - loss: 0.0023
Epoch 14/100
89/89 - 0s - loss: 0.0023
Epoch 15/100
89/89 - 0s - loss: 0.0024
Epoch 16/100
89/89 - 0s - loss: 0.0023
Epoch 17/100
89/89 - 0s - loss: 0.0022
Epoch 18/100
89/89 - 0s - loss: 0.0023
Epoch 19/100
89/89 - 0s - loss: 0.0022
Epoch 20/100
89/89 - 0s - loss: 0.0022
Epoch 21/100
89/89 - 0s - loss: 0.0022
Epoch 22/100
89/89 - 0s - loss: 0.0022
Epoch 23/100
89/89 - 0s - loss: 0.0022
Epoch 24/100
89/89 - 0s - loss: 0.0023
Epoch 25/100
89/89 - 0s - loss: 0.0022
Epoch 26/100
89/89 - 0s - loss: 0.0022
Epoch 27/100
89/89 - 0s - loss: 0.0021
Epoch 28/100
89/89 - 0s - loss: 0.0021
Epoch 29/100
89/89 - 0s - loss: 0.0021
Epoch 30/100
89/89 - 0s - loss: 0.0021
Epoch 31/100
89/89 - 0s - loss: 0.0021
Epoch 32/100
89/89 - 0s - loss: 0.0021
Epoch 33/100
89/89 - 0s - loss: 0.0020
Epoch 34/100
89/89 - 0s - loss: 0.0021
Epoch 35/100
89/89 - 0s - loss: 0.0020
Epoch 36/100
89/89 - 0s - loss: 0.0020
Epoch 37/100
89/89 - 0s - loss: 0.0021
Epoch 38/100
89/89 - 0s - loss: 0.0020
Epoch 39/100
89/89 - 0s - loss: 0.0020
Epoch 40/100
89/89 - 0s - loss: 0.0020
Epoch 41/100
89/89 - 0s - loss: 0.0019
Epoch 42/100
89/89 - 0s - loss: 0.0020
Epoch 43/100
89/89 - 0s - loss: 0.0019
Epoch 44/100
89/89 - 0s - loss: 0.0019
Epoch 45/100
89/89 - 0s - loss: 0.0020
Epoch 46/100
89/89 - 0s - loss: 0.0020
Epoch 47/100
89/89 - 0s - loss: 0.0019
Epoch 48/100
89/89 - 0s - loss: 0.0020
Epoch 49/100
89/89 - 0s - loss: 0.0019
Epoch 50/100
89/89 - 0s - loss: 0.0019
Epoch 51/100
89/89 - 0s - loss: 0.0018
Epoch 52/100
89/89 - 0s - loss: 0.0020
Epoch 53/100
89/89 - 0s - loss: 0.0019
Epoch 54/100
89/89 - 0s - loss: 0.0019
Epoch 55/100
89/89 - 0s - loss: 0.0018
Epoch 56/100
89/89 - 0s - loss: 0.0019
Epoch 57/100
89/89 - 0s - loss: 0.0018
Epoch 58/100
89/89 - 0s - loss: 0.0019
Epoch 59/100
89/89 - 0s - loss: 0.0019
Epoch 60/100
89/89 - 0s - loss: 0.0018
Epoch 61/100
89/89 - 0s - loss: 0.0019
Epoch 62/100
89/89 - 0s - loss: 0.0019
Epoch 63/100
89/89 - 0s - loss: 0.0018
Epoch 64/100
89/89 - 0s - loss: 0.0019
Epoch 65/100
89/89 - 0s - loss: 0.0018
Epoch 66/100
89/89 - 0s - loss: 0.0018
Epoch 67/100
89/89 - 0s - loss: 0.0018
Epoch 68/100
89/89 - 0s - loss: 0.0018
Epoch 69/100
89/89 - 0s - loss: 0.0018
Epoch 70/100
89/89 - 0s - loss: 0.0018
Epoch 71/100
89/89 - 0s - loss: 0.0018
Epoch 72/100
89/89 - 0s - loss: 0.0019
Epoch 73/100
89/89 - 0s - loss: 0.0019
Epoch 74/100
89/89 - 0s - loss: 0.0017
Epoch 75/100
89/89 - 0s - loss: 0.0018
Epoch 76/100
89/89 - 0s - loss: 0.0018
Epoch 77/100
89/89 - 0s - loss: 0.0018
Epoch 78/100
89/89 - 0s - loss: 0.0018
Epoch 79/100
89/89 - 0s - loss: 0.0018
Epoch 80/100
89/89 - 0s - loss: 0.0018
Epoch 81/100
89/89 - 0s - loss: 0.0018
Epoch 82/100
89/89 - 0s - loss: 0.0018
Epoch 83/100
89/89 - 0s - loss: 0.0018
Epoch 84/100
89/89 - 0s - loss: 0.0019
Epoch 85/100
89/89 - 0s - loss: 0.0018
Epoch 86/100
89/89 - 0s - loss: 0.0018
Epoch 87/100
89/89 - 0s - loss: 0.0018
Epoch 88/100
89/89 - 0s - loss: 0.0018
Epoch 89/100
89/89 - 0s - loss: 0.0017
Epoch 90/100
89/89 - 0s - loss: 0.0018
Epoch 91/100
89/89 - 0s - loss: 0.0018
Epoch 92/100
89/89 - 0s - loss: 0.0017
Epoch 93/100
89/89 - 0s - loss: 0.0017
Epoch 94/100
89/89 - 0s - loss: 0.0019
Epoch 95/100
89/89 - 0s - loss: 0.0017
Epoch 96/100
89/89 - 0s - loss: 0.0017
Epoch 97/100
89/89 - 0s - loss: 0.0017
Epoch 98/100
89/89 - 0s - loss: 0.0018
Epoch 99/100
89/89 - 0s - loss: 0.0018
Epoch 100/100
89/89 - 0s - loss: 0.0017
Train on 89 samples
Epoch 1/100
89/89 - 0s - loss: 0.0018
Epoch 2/100
89/89 - 0s - loss: 0.0018
Epoch 3/100
89/89 - 0s - loss: 0.0018
Epoch 4/100
89/89 - 0s - loss: 0.0018
Epoch 5/100
89/89 - 0s - loss: 0.0018
Epoch 6/100
89/89 - 0s - loss: 0.0018
Epoch 7/100
89/89 - 0s - loss: 0.0017
Epoch 8/100
89/89 - 0s - loss: 0.0017
Epoch 9/100
89/89 - 0s - loss: 0.0017
Epoch 10/100
89/89 - 0s - loss: 0.0018
Epoch 11/100
89/89 - 0s - loss: 0.0018
Epoch 12/100
89/89 - 0s - loss: 0.0018
Epoch 13/100
89/89 - 0s - loss: 0.0017
Epoch 14/100
89/89 - 0s - loss: 0.0017
Epoch 15/100
89/89 - 0s - loss: 0.0018
Epoch 16/100
89/89 - 0s - loss: 0.0017
Epoch 17/100
89/89 - 0s - loss: 0.0018
Epoch 18/100
89/89 - 0s - loss: 0.0018
Epoch 19/100
89/89 - 0s - loss: 0.0017
Epoch 20/100
89/89 - 0s - loss: 0.0017
Epoch 21/100
89/89 - 0s - loss: 0.0018
Epoch 22/100
89/89 - 0s - loss: 0.0017
Epoch 23/100
89/89 - 0s - loss: 0.0019
Epoch 24/100
89/89 - 0s - loss: 0.0017
Epoch 25/100
89/89 - 0s - loss: 0.0017
Epoch 26/100
89/89 - 0s - loss: 0.0017
Epoch 27/100
89/89 - 0s - loss: 0.0017
Epoch 28/100
89/89 - 0s - loss: 0.0017
Epoch 29/100
89/89 - 0s - loss: 0.0017
Epoch 30/100
89/89 - 0s - loss: 0.0017
Epoch 31/100
89/89 - 0s - loss: 0.0017
Epoch 32/100
89/89 - 0s - loss: 0.0017
Epoch 33/100
89/89 - 0s - loss: 0.0017
Epoch 34/100
89/89 - 0s - loss: 0.0019
Epoch 35/100
89/89 - 0s - loss: 0.0017
Epoch 36/100
89/89 - 0s - loss: 0.0018
Epoch 37/100
89/89 - 0s - loss: 0.0018
Epoch 38/100
89/89 - 0s - loss: 0.0017
Epoch 39/100
89/89 - 0s - loss: 0.0017
Epoch 40/100
89/89 - 0s - loss: 0.0017
Epoch 41/100
89/89 - 0s - loss: 0.0018
Epoch 42/100
89/89 - 0s - loss: 0.0017
Epoch 43/100
89/89 - 0s - loss: 0.0017
Epoch 44/100
89/89 - 0s - loss: 0.0016
Epoch 45/100
89/89 - 0s - loss: 0.0018
Epoch 46/100
89/89 - 0s - loss: 0.0017
Epoch 47/100
89/89 - 0s - loss: 0.0017
Epoch 48/100
89/89 - 0s - loss: 0.0018
Epoch 49/100
89/89 - 0s - loss: 0.0017
Epoch 50/100
89/89 - 0s - loss: 0.0018
Epoch 51/100
89/89 - 0s - loss: 0.0017
Epoch 52/100
89/89 - 0s - loss: 0.0017
Epoch 53/100
89/89 - 0s - loss: 0.0016
Epoch 54/100
89/89 - 0s - loss: 0.0018
Epoch 55/100
89/89 - 0s - loss: 0.0017
Epoch 56/100
89/89 - 0s - loss: 0.0017
Epoch 57/100
89/89 - 0s - loss: 0.0017
Epoch 58/100
89/89 - 0s - loss: 0.0018
Epoch 59/100
89/89 - 0s - loss: 0.0017
Epoch 60/100
89/89 - 0s - loss: 0.0017
Epoch 61/100
89/89 - 0s - loss: 0.0018
Epoch 62/100
89/89 - 0s - loss: 0.0018
Epoch 63/100
89/89 - 0s - loss: 0.0017
Epoch 64/100
89/89 - 0s - loss: 0.0017
Epoch 65/100
89/89 - 0s - loss: 0.0016
Epoch 66/100
89/89 - 0s - loss: 0.0017
Epoch 67/100
89/89 - 0s - loss: 0.0018
Epoch 68/100
89/89 - 0s - loss: 0.0017
Epoch 69/100
89/89 - 0s - loss: 0.0018
Epoch 70/100
89/89 - 0s - loss: 0.0018
Epoch 71/100
89/89 - 0s - loss: 0.0017
Epoch 72/100
89/89 - 0s - loss: 0.0018
Epoch 73/100
89/89 - 0s - loss: 0.0016
Epoch 74/100
89/89 - 0s - loss: 0.0017
Epoch 75/100
89/89 - 0s - loss: 0.0017
Epoch 76/100
89/89 - 0s - loss: 0.0017
Epoch 77/100
89/89 - 0s - loss: 0.0017
Epoch 78/100
89/89 - 0s - loss: 0.0017
Epoch 79/100
89/89 - 0s - loss: 0.0017
Epoch 80/100
89/89 - 0s - loss: 0.0017
Epoch 81/100
89/89 - 0s - loss: 0.0017
Epoch 82/100
89/89 - 0s - loss: 0.0017
Epoch 83/100
89/89 - 0s - loss: 0.0017
Epoch 84/100
89/89 - 0s - loss: 0.0018
Epoch 85/100
89/89 - 0s - loss: 0.0016
Epoch 86/100
89/89 - 0s - loss: 0.0018
Epoch 87/100
89/89 - 0s - loss: 0.0017
Epoch 88/100
89/89 - 0s - loss: 0.0017
Epoch 89/100
89/89 - 0s - loss: 0.0017
Epoch 90/100
89/89 - 0s - loss: 0.0019
Epoch 91/100
89/89 - 0s - loss: 0.0017
Epoch 92/100
89/89 - 0s - loss: 0.0017
Epoch 93/100
89/89 - 0s - loss: 0.0017
Epoch 94/100
89/89 - 0s - loss: 0.0017
Epoch 95/100
89/89 - 0s - loss: 0.0017
Epoch 96/100
89/89 - 0s - loss: 0.0016
Epoch 97/100
89/89 - 0s - loss: 0.0017
Epoch 98/100
89/89 - 0s - loss: 0.0017
Epoch 99/100
89/89 - 0s - loss: 0.0017
Epoch 100/100
89/89 - 0s - loss: 0.0017
Train Score: 20.78 RMSE
Test Score: 43.30 RMSE
Tenga cuidado, en este ejemplo la diferencia en los errores no es un indicador real de lo bueno o lo malo que sea el modelo, esto ya que el tamaño de la ventana solo está siendo puesta para ilustrar el concepto, realmente deberíamos hacer una busqueda adecuada de los hiperparámetros.
También es posible generar los datos como pasos de tiempo y no como ventanas, esta forma de entregar los datos a la red es más cercana al problema que estamos tratando de series de tiempo.
Podemos conseguir esto usando la misma representación de los datos que tenemos para las ventanas, pero en ahora el reshape se hará diferente
In [0]:
# el reshape ahora será [samples, time steps, features]
look_back = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))
In [139]:
print(trainX[:10])
[[[0.02]
[0.03]
[0.05]]
[[0.03]
[0.05]
[0.05]]
[[0.05]
[0.05]
[0.03]]
[[0.05]
[0.03]
[0.06]]
[[0.03]
[0.06]
[0.08]]
[[0.06]
[0.08]
[0.08]]
[[0.08]
[0.08]
[0.06]]
[[0.08]
[0.06]
[0.03]]
[[0.06]
[0.03]
[0. ]]
[[0.03]
[0. ]
[0.03]]]
Crearemos un modelo igual al usado anteriormente, con la diferencia que el shape de la entrada ahora será un vector columna de la forma [look_back, 1] (para las ventanas recuerde que es [1,look_back])
In [140]:
model = Sequential()
model.add(LSTM(4, input_shape=(look_back, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
Train on 89 samples
Epoch 1/100
89/89 - 2s - loss: 0.0209
Epoch 2/100
89/89 - 1s - loss: 0.0098
Epoch 3/100
89/89 - 1s - loss: 0.0086
Epoch 4/100
89/89 - 1s - loss: 0.0074
Epoch 5/100
89/89 - 1s - loss: 0.0066
Epoch 6/100
89/89 - 1s - loss: 0.0058
Epoch 7/100
89/89 - 1s - loss: 0.0052
Epoch 8/100
89/89 - 1s - loss: 0.0046
Epoch 9/100
89/89 - 1s - loss: 0.0043
Epoch 10/100
89/89 - 1s - loss: 0.0041
Epoch 11/100
89/89 - 1s - loss: 0.0038
Epoch 12/100
89/89 - 1s - loss: 0.0035
Epoch 13/100
89/89 - 1s - loss: 0.0035
Epoch 14/100
89/89 - 1s - loss: 0.0034
Epoch 15/100
89/89 - 1s - loss: 0.0033
Epoch 16/100
89/89 - 1s - loss: 0.0034
Epoch 17/100
89/89 - 1s - loss: 0.0033
Epoch 18/100
89/89 - 1s - loss: 0.0033
Epoch 19/100
89/89 - 1s - loss: 0.0032
Epoch 20/100
89/89 - 1s - loss: 0.0032
Epoch 21/100
89/89 - 1s - loss: 0.0032
Epoch 22/100
89/89 - 1s - loss: 0.0032
Epoch 23/100
89/89 - 1s - loss: 0.0032
Epoch 24/100
89/89 - 1s - loss: 0.0031
Epoch 25/100
89/89 - 1s - loss: 0.0032
Epoch 26/100
89/89 - 1s - loss: 0.0031
Epoch 27/100
89/89 - 1s - loss: 0.0031
Epoch 28/100
89/89 - 1s - loss: 0.0031
Epoch 29/100
89/89 - 1s - loss: 0.0031
Epoch 30/100
89/89 - 1s - loss: 0.0032
Epoch 31/100
89/89 - 1s - loss: 0.0031
Epoch 32/100
89/89 - 1s - loss: 0.0030
Epoch 33/100
89/89 - 1s - loss: 0.0031
Epoch 34/100
89/89 - 1s - loss: 0.0031
Epoch 35/100
89/89 - 1s - loss: 0.0030
Epoch 36/100
89/89 - 1s - loss: 0.0030
Epoch 37/100
89/89 - 1s - loss: 0.0030
Epoch 38/100
89/89 - 1s - loss: 0.0030
Epoch 39/100
89/89 - 1s - loss: 0.0030
Epoch 40/100
89/89 - 1s - loss: 0.0030
Epoch 41/100
89/89 - 1s - loss: 0.0029
Epoch 42/100
89/89 - 1s - loss: 0.0030
Epoch 43/100
89/89 - 1s - loss: 0.0030
Epoch 44/100
89/89 - 1s - loss: 0.0029
Epoch 45/100
89/89 - 1s - loss: 0.0029
Epoch 46/100
89/89 - 1s - loss: 0.0032
Epoch 47/100
89/89 - 1s - loss: 0.0029
Epoch 48/100
89/89 - 1s - loss: 0.0029
Epoch 49/100
89/89 - 1s - loss: 0.0028
Epoch 50/100
89/89 - 1s - loss: 0.0029
Epoch 51/100
89/89 - 1s - loss: 0.0029
Epoch 52/100
89/89 - 1s - loss: 0.0028
Epoch 53/100
89/89 - 1s - loss: 0.0029
Epoch 54/100
89/89 - 1s - loss: 0.0029
Epoch 55/100
89/89 - 1s - loss: 0.0028
Epoch 56/100
89/89 - 1s - loss: 0.0029
Epoch 57/100
89/89 - 1s - loss: 0.0028
Epoch 58/100
89/89 - 1s - loss: 0.0029
Epoch 59/100
89/89 - 1s - loss: 0.0028
Epoch 60/100
89/89 - 1s - loss: 0.0030
Epoch 61/100
89/89 - 1s - loss: 0.0028
Epoch 62/100
89/89 - 1s - loss: 0.0029
Epoch 63/100
89/89 - 1s - loss: 0.0028
Epoch 64/100
89/89 - 1s - loss: 0.0030
Epoch 65/100
89/89 - 1s - loss: 0.0027
Epoch 66/100
89/89 - 1s - loss: 0.0027
Epoch 67/100
89/89 - 1s - loss: 0.0027
Epoch 68/100
89/89 - 1s - loss: 0.0028
Epoch 69/100
89/89 - 1s - loss: 0.0027
Epoch 70/100
89/89 - 1s - loss: 0.0026
Epoch 71/100
89/89 - 1s - loss: 0.0027
Epoch 72/100
89/89 - 1s - loss: 0.0027
Epoch 73/100
89/89 - 1s - loss: 0.0027
Epoch 74/100
89/89 - 1s - loss: 0.0027
Epoch 75/100
89/89 - 1s - loss: 0.0026
Epoch 76/100
89/89 - 1s - loss: 0.0025
Epoch 77/100
89/89 - 1s - loss: 0.0026
Epoch 78/100
89/89 - 1s - loss: 0.0025
Epoch 79/100
89/89 - 1s - loss: 0.0025
Epoch 80/100
89/89 - 1s - loss: 0.0026
Epoch 81/100
89/89 - 1s - loss: 0.0026
Epoch 82/100
89/89 - 1s - loss: 0.0025
Epoch 83/100
89/89 - 1s - loss: 0.0026
Epoch 84/100
89/89 - 1s - loss: 0.0025
Epoch 85/100
89/89 - 1s - loss: 0.0024
Epoch 86/100
89/89 - 1s - loss: 0.0024
Epoch 87/100
89/89 - 1s - loss: 0.0024
Epoch 88/100
89/89 - 1s - loss: 0.0023
Epoch 89/100
89/89 - 1s - loss: 0.0025
Epoch 90/100
89/89 - 1s - loss: 0.0023
Epoch 91/100
89/89 - 1s - loss: 0.0024
Epoch 92/100
89/89 - 1s - loss: 0.0023
Epoch 93/100
89/89 - 1s - loss: 0.0023
Epoch 94/100
89/89 - 1s - loss: 0.0023
Epoch 95/100
89/89 - 1s - loss: 0.0022
Epoch 96/100
89/89 - 1s - loss: 0.0023
Epoch 97/100
89/89 - 1s - loss: 0.0023
Epoch 98/100
89/89 - 1s - loss: 0.0023
Epoch 99/100
89/89 - 1s - loss: 0.0022
Epoch 100/100
89/89 - 1s - loss: 0.0022
Out[140]:
<tensorflow.python.keras.callbacks.History at 0x7f63170fffd0>
In [0]:
#hacemos una predicción
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# Reescalemos
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculamos el error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
# Graficamos
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
Train Score: 25.42 RMSE
Test Score: 55.86 RMSE
Esta última forma de entregar los datos suele tener un mejor performance en general, ya que tiene más sentido para el problema y para el diseño de la red que las ventanas.
Existen además de las ventanas y los pasos de tiempo otro par de técnicas más avanzadas en el tratamiento de estos problemas como la memoria entre batches o la memoria entre batches apiladas Un ejemplo de estas.
In [0]:
Usando lo aprendido sobre embedding y arquitecturas many-to-one realizar una analisis de sentimientos con el sigueinte dataset https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews
Tenga encuenta que tendra que aplicar lo aprendido hasta ahora sobre preprocesamiento de texto
In [0]:
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
Importemos algunas de las librerias que nececitaremos
In [0]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from tensorflow import keras
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import seaborn as sns
In [0]:
path = '/content/drive/My Drive/DataSets/IMBD /IMDB Dataset.csv'
Importemos el dataset usando pandas
In [0]:
data_set = pd.read_csv(path)
In [0]:
data_set.shape
Out[0]:
(50000, 2)
In [0]:
data_set.head()
Out[0]:
review
sentiment
0
One of the other reviewers has mentioned that ...
positive
1
A wonderful little production. <br /><br />The...
positive
2
I thought this was a wonderful way to spend ti...
positive
3
Basically there's a family where a little boy ...
negative
4
Petter Mattei's "Love in the Time of Money" is...
positive
In [0]:
len(data_set['review'][0])
Out[0]:
1761
In [0]:
len(data_set['review'][1])
Out[0]:
998
In [0]:
data_train = data_set.iloc[0:25000,:]
data_test = data_set.iloc[25000:,:]
In [0]:
data_train.shape
Out[0]:
(25000, 2)
In [0]:
data_test.shape
Out[0]:
(25000, 2)
In [0]:
data_train.groupby('sentiment').count()/25000
Out[0]:
review
sentiment
negative
0.50104
positive
0.49896
In [0]:
sns.countplot(data_train['sentiment'])
Out[0]:
<matplotlib.axes._subplots.AxesSubplot at 0x7feb73cd1630>
Ahora con lo aprendido anteriormente , relicemos una limpieza de los datos
In [0]:
tokenizer = Tokenizer(num_words=10000)
In [0]:
tokenizer.fit_on_texts(data_train['review'])
In [0]:
#sequences = tokenizer.texts_to_sequences(data_set['review'])
In [0]:
X_train = tokenizer.texts_to_sequences(data_train['review'])
X_test = tokenizer.texts_to_sequences(data_test['review'])
In [0]:
X_train
Out[0]:
[[27,
4,
1,
77,
2019,
45,
1006,
12,
100,
147,
39,
318,
3335,
425,
450,
26,
3076,
33,
23,
197,
14,
11,
6,
602,
48,
596,
15,
69,
7,
7,
1,
87,
148,
12,
3387,
69,
41,
3335,
13,
92,
5350,
2,
133,
4,
543,
60,
271,
8,
197,
36,
1,
649,
139,
1686,
69,
11,
6,
21,
3,
118,
16,
1,
8001,
2225,
38,
11,
118,
2518,
54,
5451,
15,
5151,
5,
1345,
383,
38,
543,
92,
6,
3879,
8,
1,
354,
346,
4,
1,
649,
7,
7,
9,
6,
432,
3335,
14,
12,
6,
1,
356,
5,
1,
5769,
2713,
1178,
9,
2754,
1405,
20,
525,
32,
4755,
2678,
4,
1,
1236,
119,
29,
1,
6968,
25,
3090,
2,
385,
34,
6,
21,
316,
20,
1,
4476,
7279,
525,
6,
350,
5,
105,
7179,
4438,
8002,
2289,
2,
50,
34,
326,
8151,
6618,
2,
9654,
23,
110,
236,
241,
7,
7,
10,
58,
132,
1,
283,
1298,
4,
1,
118,
6,
662,
5,
1,
196,
12,
9,
279,
119,
77,
273,
594,
2922,
820,
177,
1391,
4107,
16,
2439,
1191,
820,
1451,
820,
879,
3335,
152,
972,
185,
1,
87,
425,
10,
123,
217,
3387,
69,
14,
34,
1539,
9,
13,
2295,
10,
415,
132,
10,
13,
1573,
16,
9,
18,
14,
10,
291,
50,
10,
1420,
3,
1316,
16,
3335,
2,
192,
9860,
5,
1,
316,
2120,
4,
2103,
543,
21,
39,
543,
18,
8425,
7180,
4873,
26,
2727,
42,
16,
3,
6619,
500,
20,
628,
2,
74,
241,
15,
9,
68,
7529,
659,
716,
6619,
109,
665,
84,
1236,
662,
5,
63,
560,
4,
915,
2121,
38,
1236,
550,
147,
3335,
22,
198,
424,
4053,
15,
48,
6,
3388,
802,
1643,
43,
22,
66,
74,
8,
1227,
15,
124,
3991,
493],
[3,
389,
115,
352,
7,
7,
1,
1387,
2999,
6,
52,
52,
159,
55,
2311,
1591,
2,
405,
3,
2,
513,
285,
4,
1855,
5,
1,
448,
412,
7,
7,
1,
149,
23,
551,
68,
2156,
533,
4512,
21,
61,
45,
192,
29,
1,
18,
28,
45,
29,
1,
2359,
174,
3237,
97,
22,
66,
364,
64,
1,
787,
9082,
31,
1,
1831,
5,
7530,
6526,
21,
61,
6,
9,
68,
274,
1,
147,
18,
9,
6,
3,
411,
2,
2393,
412,
3,
4298,
352,
41,
27,
4,
1,
83,
4,
206,
2,
24,
117,
7,
7,
1,
1855,
65,
270,
350,
15,
1,
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In [0]:
X_train = pad_sequences(X_train, maxlen=500)
X_test = pad_sequences(X_test, maxlen=500)
In [0]:
len(np.unique(X_train))
Out[0]:
10000
In [0]:
y_train = data_train['sentiment'].map({'positive':1, 'negative':0 })
y_test = data_test['sentiment'].map({'positive':1, 'negative':0 })
Ahora procedamos a crear el modelo
In [0]:
model = keras.models.Sequential([
keras.layers.Embedding(input_dim=10000 , output_dim=32, input_length=500),
keras.layers.LSTM(20),
keras.layers.Dense(1, activation='sigmoid')
])
In [0]:
model.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_2 (Embedding) (None, 500, 32) 320000
_________________________________________________________________
lstm_2 (LSTM) (None, 20) 4240
_________________________________________________________________
dense_2 (Dense) (None, 1) 21
=================================================================
Total params: 324,261
Trainable params: 324,261
Non-trainable params: 0
_________________________________________________________________
In [0]:
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])
In [0]:
model.fit(X_train,y_train, epochs=5, batch_size=512, validation_data=(X_test,y_test))
Train on 25000 samples, validate on 25000 samples
Epoch 1/5
25000/25000 [==============================] - 60s 2ms/sample - loss: 0.6739 - acc: 0.6460 - val_loss: 0.5660 - val_acc: 0.7724
Epoch 2/5
25000/25000 [==============================] - 58s 2ms/sample - loss: 0.4543 - acc: 0.8056 - val_loss: 0.3785 - val_acc: 0.8486
Epoch 3/5
25000/25000 [==============================] - 58s 2ms/sample - loss: 0.2959 - acc: 0.8862 - val_loss: 0.3048 - val_acc: 0.8770
Epoch 4/5
25000/25000 [==============================] - 58s 2ms/sample - loss: 0.2165 - acc: 0.9230 - val_loss: 0.2987 - val_acc: 0.8845
Epoch 5/5
25000/25000 [==============================] - 59s 2ms/sample - loss: 0.1730 - acc: 0.9443 - val_loss: 0.2938 - val_acc: 0.8857
Out[0]:
<tensorflow.python.keras.callbacks.History at 0x7feb731d3e48>
Ahora hagamos una prueba en una comentario
In [0]:
comentario_1 = ["I've never seen anything so bad in this world"]
comentario_2 = ["The moveio was not good"]
In [0]:
comentario = tokenizer.texts_to_sequences(comentario_2)
In [0]:
comentario = pad_sequences(comentario, maxlen=500)
In [0]:
model.predict(comentario)
Out[0]:
array([[0.55814886]], dtype=float32)
In [0]:
model.predict_classes(comentario)
Out[0]:
array([[1]], dtype=int32)
In [0]:
Basado en el ejemplo de forecasting, prediga el precio de stock ( de apertura) para el sigueinte dataset 'https://raw.githubusercontent.com/mwitiderrick/stockprice/master/NSE-TATAGLOBAL.csv'. Primero implmente una arquitectura many-to-one, para predecir el valores de stock un tiempo adelante, lego convierta el problema en un many-to-many para predecir el valor de stock 10 pasos temporales adelante
In [0]:
%tensorflow_version 2.x
TensorFlow is already loaded. Please restart the runtime to change versions.
In [0]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tensorflow import keras
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.model_selection import train_test_split
carguemos los datos usando pandas
In [0]:
url = 'https://raw.githubusercontent.com/mwitiderrick/stockprice/master/NSE-TATAGLOBAL.csv'
data_set = pd.read_csv(url)
In [0]:
data_set.head(2)
Out[0]:
Date
Open
High
Low
Last
Close
Total Trade Quantity
Turnover (Lacs)
0
2018-09-28
234.05
235.95
230.2
233.5
233.75
3069914
7162.35
1
2018-09-27
234.55
236.80
231.1
233.8
233.25
5082859
11859.95
In [0]:
data_set.tail(2)
Out[0]:
Date
Open
High
Low
Last
Close
Total Trade Quantity
Turnover (Lacs)
2033
2010-07-22
120.3
122.0
120.25
120.75
120.90
293312
355.17
2034
2010-07-21
122.1
123.0
121.05
121.10
121.55
658666
803.56
In [0]:
data_set.shape
Out[0]:
(2035, 8)
coloquemos el dataset en orden ascendente
In [0]:
data_set = data_set.reindex(index= data_set.index[::-1])
In [0]:
plt.plot(data_set['Open'])
Out[0]:
[<matplotlib.lines.Line2D at 0x7feb7309b630>]
In [0]:
plt.plot(np.arange(0,60),data_set.iloc[:60,1])
plt.plot(61,data_set.iloc[60,1],'r*')
Out[0]:
[<matplotlib.lines.Line2D at 0x7feb72fafe48>]
normalicemos los datos
In [0]:
data_set = data_set[['Open']]
scale = StandardScaler()
data_set = scale.fit_transform(data_set)
ahora prosedamos a contruir el dataset con el cual vamos a entrenar , este debe tener las dimensiones [sample, timestep, features]
In [0]:
X = []
y = []
for i in np.arange(60,2035):
X.append(data_set[i-60:i,0])
y.append(data_set[i,0])
X = np.array(X)
y = np.array(y)
In [0]:
X = X.reshape((-1,60,1))
In [0]:
y = y.reshape((-1,1))
In [0]:
print(X.shape, y.shape)
(1975, 60, 1) (1975, 1)
In [0]:
X_train, X_test , y_train,y_test = train_test_split(X,y)
Ahora procedamos a contruir el modelo many-to-one
In [0]:
X_train.shape
Out[0]:
(1481, 60, 1)
In [0]:
model = keras.models.Sequential([
keras.layers.LSTM(20, return_sequences=True , input_shape=[None,1]),
keras.layers.LSTM(20),
keras.layers.Dense(1)
])
In [0]:
model.summary()
Model: "sequential_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_5 (LSTM) (None, None, 20) 1760
_________________________________________________________________
lstm_6 (LSTM) (None, 20) 3280
_________________________________________________________________
dense_5 (Dense) (None, 1) 21
=================================================================
Total params: 5,061
Trainable params: 5,061
Non-trainable params: 0
_________________________________________________________________
In [0]:
model.compile(loss='mse', optimizer='nadam')
In [0]:
model.fit(X_train,y_train, epochs=10, validation_split=0.2, validation_data=(X_test,y_test))
Train on 1481 samples, validate on 494 samples
Epoch 1/10
1481/1481 [==============================] - 7s 5ms/sample - loss: 0.2569 - val_loss: 0.0553
Epoch 2/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0408 - val_loss: 0.0402
Epoch 3/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0306 - val_loss: 0.0294
Epoch 4/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0271 - val_loss: 0.0261
Epoch 5/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0246 - val_loss: 0.0244
Epoch 6/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0225 - val_loss: 0.0224
Epoch 7/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0201 - val_loss: 0.0215
Epoch 8/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0188 - val_loss: 0.0235
Epoch 9/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0183 - val_loss: 0.0202
Epoch 10/10
1481/1481 [==============================] - 4s 3ms/sample - loss: 0.0165 - val_loss: 0.0185
Out[0]:
<tensorflow.python.keras.callbacks.History at 0x7feb71e10160>
Ahora evaluemos los resultados
In [0]:
y_predict = model.predict(X_test)
In [0]:
plt.plot(X_test[0,:,0])
plt.plot(60, y_predict[0], 'r*')
plt.plot(60,y_test[0], 'g*')
Out[0]:
[<matplotlib.lines.Line2D at 0x7feb716b7320>]
In [0]:
prediction = model.predict(data_set.reshape((1,-1,1)))
In [0]:
plt.plot(data_set)
plt.plot(2036,prediction, 'r*')
Out[0]:
[<matplotlib.lines.Line2D at 0x7feb7182cdd8>]
In [0]:
Content source: jdamaster/Alucinema
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