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classifier.py
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classifier.py
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import json
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras
DATA = "data.json"
NUMBER_OF_LANGUAGES = 12
def get_data(json_data):
with open(json_data, "r") as input_data:
data = json.load(input_data)
inputs = np.array(data["mfcc"])
results = np.array(data["labels"])
return inputs, results
if __name__ == "__main__":
# get data
X, y = get_data(DATA)
# create train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# build neural network
model = keras.Sequential([
# input layer
keras.layers.Flatten(input_shape=(X.shape[1], X.shape[2])),
# 1st dense layer
keras.layers.Dense(512, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
keras.layers.Dropout(0.25),
# 2nd dense layer
keras.layers.Dense(256, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
keras.layers.Dropout(0.25),
# 3rd dense layer
keras.layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
keras.layers.Dropout(0.25),
# output layer
keras.layers.Dense(NUMBER_OF_LANGUAGES, activation='softmax')
])
# compile model
optimiser = keras.optimizers.Adam(learning_rate=0.0001)
model.compile(optimizer=optimiser,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# train model
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=32, epochs=100)