Change a CNN classifier model to a CNN regression model

Question

I am trying to change a CNN classification model to a CNN regression model. The classification model had some press statements as an input and the change (0 for negative return on the release day and 1 for positive change) of an Index as the second variable. Now I am trying to change the model from a classification to a regression in the end, so that I can work with the actual returns and not with the binary classification.

So my input in the neural network looks like this:

                                                   document    VIX 1d
1999-05-18  Release Date: May 18, 1999\n\nFor immediate re... -0.010526
1999-06-30  Release Date: June 30, 1999\n\nFor immediate r... -0.082645
1999-08-24  Release Date: August 24, 1999\n\nFor immediate... -0.043144

(document will tokenizes before going in the NN, just that you have an example)

I changed so far the following parameters: - loss function is now the mean squared error (before: binary cross entropy) , the activation of the last layer now linear (before: sigmoid) and the metrics to mse (before: acc)

Below you can see my code:

 all_words = [word for tokens in X for word in tokens]
   all_sentence_lengths = [len(tokens) for tokens in X]
   ALL_VOCAB = sorted(list(set(all_words)))
   print("%s words total, with a vocabulary size of %s" % (len(all_words), len(ALL_VOCAB)))
   print("Max sentence length is %s" % max(all_sentence_lengths))


####################### CHANGE THE PARAMETERS HERE #####################################
EMBEDDING_DIM = 300 # how big is each word vector
MAX_VOCAB_SIZE = 1893# how many unique words to use (i.e num rows in embedding vector)
MAX_SEQUENCE_LENGTH = 1086 # max number of words in a comment to use


tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE, lower=True, char_level=False)
tokenizer.fit_on_texts(change_df["document"].tolist())
training_sequences = tokenizer.texts_to_sequences(X_train.tolist())

train_word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(train_word_index))

train_embedding_weights = np.zeros((len(train_word_index)+1, EMBEDDING_DIM))
for word,index in train_word_index.items():
    train_embedding_weights[index,:] = w2v_model[word] if word in w2v_model else np.random.rand(EMBEDDING_DIM)
print(train_embedding_weights.shape)


######################## TRAIN AND TEST SET #################################
train_cnn_data = pad_sequences(training_sequences, maxlen=MAX_SEQUENCE_LENGTH)
test_sequences = tokenizer.texts_to_sequences(X_test.tolist())
test_cnn_data = pad_sequences(test_sequences, maxlen=MAX_SEQUENCE_LENGTH)

def ConvNet(embeddings, max_sequence_length, num_words, embedding_dim, trainable=False, extra_conv=True):
    embedding_layer = Embedding(num_words,
                                embedding_dim,
                                weights=[embeddings],
                                input_length=max_sequence_length,
                                trainable=trainable)

    sequence_input = Input(shape=(max_sequence_length,), dtype='int32')
    embedded_sequences = embedding_layer(sequence_input)

    # Yoon Kim model (https://arxiv.org/abs/1408.5882)
    convs = []
    filter_sizes = [3, 4, 5]

    for filter_size in filter_sizes:
        l_conv = Conv1D(filters=128, kernel_size=filter_size, activation='relu')(embedded_sequences)
        l_pool = MaxPooling1D(pool_size=3)(l_conv)
        convs.append(l_pool)

    l_merge = concatenate([convs[0], convs[1], convs[2]], axis=1)

    # add a 1D convnet with global maxpooling, instead of Yoon Kim model
    conv = Conv1D(filters=128, kernel_size=3, activation='relu')(embedded_sequences)
    pool = MaxPooling1D(pool_size=3)(conv)

    if extra_conv == True:
        x = Dropout(0.5)(l_merge)
    else:
        # Original Yoon Kim model
        x = Dropout(0.5)(pool)
    x = Flatten()(x)
    x = Dense(128, activation='relu')(x)
    preds = Dense(1, activation='linear')(x)

    model = Model(sequence_input, preds)
    model.compile(loss='mean_squared_error',
                  optimizer='adadelta',
                  metrics=['mse'])
    model.summary()
    return model

x_train = train_cnn_data
y_tr = y_train
x_test = test_cnn_data

model = ConvNet(train_embedding_weights, MAX_SEQUENCE_LENGTH, len(train_word_index)+1, EMBEDDING_DIM, False)

#define callbacks
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0.01, patience=4, verbose=1)
callbacks_list = [early_stopping]

hist = model.fit(x_train, y_tr, epochs=5, batch_size=33, validation_split=0.1, shuffle=True, callbacks=callbacks_list)

y_tes=model.predict(x_test, batch_size=33, verbose=1)

Does someone has an idea what else should I change as the code is working, but I have very poor results I think.. Like running the code gives me the following result:

Epoch 5/5

 33/118 [=======>......................] - ETA: 15s - loss: 0.0039 - mse: 0.0039
 66/118 [===============>..............] - ETA: 9s - loss: 0.0031 - mse: 0.0031 
 99/118 [========================>.....] - ETA: 3s - loss: 0.0034 - mse: 0.0034
118/118 [==============================] - 22s 189ms/step - loss: 0.0035 - mse: 0.0035 - val_loss: 0.0060 - val_mse: 0.0060

Or at least a source where I can read something? I just find some classification CNNs on the web, but no example actually NLP CNN with a regression.

Thanks a lot,

Lukas

Answer 1

This is a great example. Copy/paste the code, load the datasets; it should answer all of your questions.

# Classification with Tensorflow 2.0
import pandas as pd
import numpy as np
import tensorflow as tf

import matplotlib.pyplot as plt
# %matplotlib inline

import seaborn as sns
sns.set(style="darkgrid")

cols = ['price', 'maint', 'doors', 'persons', 'lug_capacity', 'safety', 'output']
cars = pd.read_csv(r'C:\\your_path\\cars_dataset.csv', names=cols, header=None)

cars.head()


price = pd.get_dummies(cars.price, prefix='price')
maint = pd.get_dummies(cars.maint, prefix='maint')

doors = pd.get_dummies(cars.doors, prefix='doors')
persons = pd.get_dummies(cars.persons, prefix='persons')

lug_capacity = pd.get_dummies(cars.lug_capacity, prefix='lug_capacity')
safety = pd.get_dummies(cars.safety, prefix='safety')

labels = pd.get_dummies(cars.output, prefix='condition')

# To create our feature set, we can merge the first six columns horizontally:

X = pd.concat([price, maint, doors, persons, lug_capacity, safety] , axis=1)

# Let's see how our label column looks now:

labels.head()


y = labels.values

# The final step before we can train our TensorFlow 2.0 classification model is to divide the dataset into training and test sets:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

# Model Training

# To train the model, let's import the TensorFlow 2.0 classes. Execute the following script:

from tensorflow.keras.layers import Input, Dense, Activation,Dropout
from tensorflow.keras.models import Model


# The next step is to create our classification model:
input_layer = Input(shape=(X.shape[1],))
dense_layer_1 = Dense(15, activation='relu')(input_layer)
dense_layer_2 = Dense(10, activation='relu')(dense_layer_1)
output = Dense(y.shape[1], activation='softmax')(dense_layer_2)

model = Model(inputs=input_layer, outputs=output)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])


# The following script shows the model summary:

print(model.summary())


# Result:

# Model: "model"
# Layer (type)                 Output Shape              Param #   


# Finally, to train the model execute the following script:
history = model.fit(X_train, y_train, batch_size=8, epochs=50, verbose=1, validation_split=0.2)


# Result:

# Train on 7625 samples, validate on 1907 samples
# Epoch 1/50
# - 4s 492us/sample - loss: 3.0998 - acc: 0.2658 - val_loss: 12.4542 - val_acc: 0.0834


# Let's finally evaluate the performance of our classification model on the test set:

score = model.evaluate(X_test, y_test, verbose=1)

print("Test Score:", score[0])
print("Test Accuracy:", score[1])


# Result: 



# Regression with TensorFlow 2.0

petrol_cons = pd.read_csv(r'C:\\your_path\\gas_consumption.csv')

# Let's print the first five rows of the dataset via the head() function:

petrol_cons.head()


X = petrol_cons.iloc[:, 0:4].values
y = petrol_cons.iloc[:, 4].values

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)


# Model Training

# The next step is to train our model. This is process is quite similar to training the classification. The only change will be in the loss function and the number of nodes in the output dense layer. Since now we are predicting a single continuous value, the output layer will only have 1 node.

input_layer = Input(shape=(X.shape[1],))
dense_layer_1 = Dense(100, activation='relu')(input_layer)
dense_layer_2 = Dense(50, activation='relu')(dense_layer_1)
dense_layer_3 = Dense(25, activation='relu')(dense_layer_2)
output = Dense(1)(dense_layer_3)

model = Model(inputs=input_layer, outputs=output)
model.compile(loss="mean_squared_error" , optimizer="adam", metrics=["mean_squared_error"])


# Finally, we can train the model with the following script:

history = model.fit(X_train, y_train, batch_size=2, epochs=100, verbose=1, validation_split=0.2)


# Result:

# Train on 30 samples, validate on 8 samples
# Epoch 1/100


# To evaluate the performance of a regression model on test set, one of the most commonly used metrics is root mean squared error. We can find mean squared error between the predicted and actual values via the mean_squared_error class of the sklearn.metrics module. We can then take square root of the resultant mean squared error. Look at the following script:

from sklearn.metrics import mean_squared_error
from math import sqrt

pred_train = model.predict(X_train)
print(np.sqrt(mean_squared_error(y_train,pred_train)))


# Result:

# 57.398156439652396


pred = model.predict(X_test)
print(np.sqrt(mean_squared_error(y_test,pred)))


# Result:

# 86.61012708343948


# https://stackabuse.com/tensorflow-2-0-solving-classification-and-regression-problems/
# datasets:
# https://www.kaggle.com/elikplim/car-evaluation-data-set


# for OLS analysis
import statsmodels.api as sm

model = sm.OLS(y, X)
results = model.fit()
print(results.summary())

# Results:
                                 OLS Regression Results                                
=======================================================================================
Dep. Variable:                      y   R-squared (uncentered):                   0.987
Model:                            OLS   Adj. R-squared (uncentered):              0.986
Method:                 Least Squares   F-statistic:                              867.8
Date:                Thu, 09 Apr 2020   Prob (F-statistic):                    3.17e-41
Time:                        13:13:11   Log-Likelihood:                         -269.00
No. Observations:                  48   AIC:                                      546.0
Df Residuals:                      44   BIC:                                      553.5
Df Model:                           4                                                  
Covariance Type:            nonrobust                                                  
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
x1           -14.2390      8.414     -1.692      0.098     -31.196       2.718
x2            -0.0594      0.017     -3.404      0.001      -0.095      -0.024
x3             0.0012      0.003      0.404      0.688      -0.005       0.007
x4          1630.8913    130.969     12.452      0.000    1366.941    1894.842
==============================================================================
Omnibus:                        9.750   Durbin-Watson:                   2.226
Prob(Omnibus):                  0.008   Jarque-Bera (JB):                9.310
Skew:                           0.880   Prob(JB):                      0.00952
Kurtosis:                       4.247   Cond. No.                     1.00e+05
==============================================================================

data sources:

https://www.kaggle.com/elikplim/car-evaluation-data-set

https://drive.google.com/file/d/1mVmGNx6cbfvRHC_DvF12ZL3wGLSHD9f_/view

Answer 2

Maybe two more questions: 1. You are getting quite high numbers for the regression root mean squared error. (57.39 and 86.61) & I get (for my dataset) 0.0851 (train) and 0.1169 (test). Seems that my values are quite good, right? The lower mean root mean squared error, the better or? I had my statistics class quite a while ago... :D 2. Do you maybe even know (or maybe you have an example), how I would have to implement another variable in the regression in a neural network? In my case, I have text data and returns I want to predict. I would like to include some macroeconomic (control)variables as well.. Thanks!