classifier.py

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    data_words, data_plant_types, test_size=0.33, random_state=42)

# Build a neural network
network = input_data(shape=[None, len(data_words[0])])
network = fully_connected(network, 2048, activation='relu')
network = fully_connected(
    network, len(data_plant_types[0]), activation='softmax')

network = regression(network, optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=0.0003)
model = tflearn.DNN(network, tensorboard_verbose=0)

# Train the network
model.fit(X_train, y_train, n_epoch=10, shuffle=True,
          validation_set=(X_test, y_test),
          show_metric=True, batch_size=25, run_id='specific_cnn')