Using Convolutions on the IRIS dataset

Question

In the famous iris dataset, I have tried, different ML models such as linear regression, SVM, Decision Trees, and Random Forests. I want to use a convolution network. I saw this in a quora post and found it to be interesting. This is just for curiosity, but how can I implement this?
Post link : https://www.quora.com/How-can-I-train-an-Iris-data-set-in-a-CNN-algorithm

Asked By: dark_knight_with_i

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Source

Answer 1

You can use the code below:

# Create your features and Y
X = np.array(df.drop("Species", axis=1))

y = np.array(df.Species).reshape(-1, 1)


# Encoding Target Y to One-Hot format
encoder = OneHotEncoder(sparse=False)
y = encoder.fit_transform(y)

# You can then use train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y)

# Important to reshape your data here to feed it to the CNN.
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))


# Build the model and train
model = tf.keras.Sequential()

model.add(tf.keras.layers.Convolution1D(filters=2, kernel_size=1,input_shape=(4,1), name='Conv1'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation='relu', name='Dense1'))
model.add(tf.keras.layers.Dense(10, activation='relu', name='Dense2'))
model.add(tf.keras.layers.Dense(3, activation='softmax', name='output'))

optimizer = tf.keras.optimizers.Adam(lr=0.001)
model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

model.summary()

model.fit(X_train, y_train, validation_data=(X_test, y_test) ,epochs=1000,) 


# Test preditions
y_pred = model.predict(X_test)

# Get argmax of both y_test and y_pred to get classes
y_pred = np.argmax(y_pred, axis=1)
y_test = np.argmax(y_test, axis=1)

# Accuracy and Classification Report
print(f"Accuracy:{accuracy_score(y_test, y_pred)*100}%")
print(classification_report(y_test, y_pred))

Answered By: L_Jay

Using Convolutions on the IRIS dataset

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