ebot64/keras-multiclass-classification

Multiclass classification using keras using iris flowers database. Unlike in binary classification, there are more than two classes to be predicted. Therefore, the choice of activation function differs for better model performance

Multiclass classification problem

Problem Description

This is the Iris iris flowers dataset problem. In this problem, there are more than two classes to be predicted.

Import Classes and Functions

#import tensorflow as tf

import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from scikeras.wrappers import KerasClassifier
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline

Load the Dataset

# load dataset
df = pd.read_csv("iris.csv", header=None)
df.head()

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	0	1	2	3	4
0	5.1	3.5	1.4	0.2	Setosa
1	4.9	3.0	1.4	0.2	Setosa
2	4.7	3.2	1.3	0.2	Setosa
3	4.6	3.1	1.5	0.2	Setosa
4	5.0	3.6	1.4	0.2	Setosa

dataset = df.values
X = dataset[:,0:4].astype(float)
Y = dataset[:,4]

Encode the Output Variable

# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one-hot encoded)
dummy_y = to_categorical(encoded_Y)

Define the neural network model

# define baseline model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(8, input_shape=(4,), activation='relu'))
    model.add(Dense(3, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

estimator = KerasClassifier(model=baseline_model, epochs=200, batch_size=5, verbose=0)

Evaluate the Model with k-Fold Cross-Validation

kfold = KFold(n_splits=10, shuffle=True)

results = cross_val_score(estimator, X, dummy_y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))

WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002225B587880> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000002225EE53EB0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
Baseline: 96.67% (6.15%)

for r in results:
    print(r)
#type(results)

0.9333333333333333
1.0
0.9333333333333333
1.0
0.8
1.0
1.0
1.0
1.0
1.0