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Update for changes to keras and tensorflow #1

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Update for changes to keras and tensorflow #1

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280ec1d
uptodate with TF2_5 and above
Oct 21, 2021
8aa9471
remove comments
Oct 21, 2021
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15 changes: 8 additions & 7 deletions test.py
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Original file line number Diff line number Diff line change
@@ -1,17 +1,18 @@
import keras
from sklearn.metrics import classification_report
from utils import wav2mfcc, model, get_data
from keras.utils import to_categorical
import numpy as np
import tensorflow as tf

def check_preds(X, y):

trained_model = keras.models.load_model('trained_model.h5')
predictions = trained_model.predict_classes(X)
predictions = trained_model.predict(X)
classes_x = np.argmax(predictions, axis=1)
print(classification_report(y, tf.keras.utils.to_categorical(classes_x)))

print(classification_report(y, to_categorical(predictions)))

if __name__ == '__main__':
_, X_test, _, y_test, _ = get_data.get_all()

# if __name__ == '__main__':
# _, X_test, _, y_test, _ = get_data.get_all()
#
# check_preds(X_test, y_test)
check_preds(X_test, y_test)
2 changes: 1 addition & 1 deletion utils/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ def get_cnn_model(input_shape, num_classes):
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])
model.compile(loss="categorical_crossentropy", optimizer='Adadelta', metrics=['accuracy'])

return model

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4 changes: 2 additions & 2 deletions utils/wav2mfcc.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
import numpy as np
import librosa
import os
from keras.utils import to_categorical
import tensorflow as tf

def wav2mfcc(file_path, max_pad_len=20):
wave, sr = librosa.load(file_path, mono=True, sr=None)
Expand All @@ -25,7 +25,7 @@ def get_data():
label = f.split('_')[0]
labels.append(label)

return np.asarray(mfccs), to_categorical(labels)
return np.asarray(mfccs), tf.keras.utils.to_categorical(labels)

# if __name__ == '__main__':
# mfccs, labels = get_data()
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