def train():
x = np.load('data/x_train.npy')
y = np.load('data/y_train.npy')
y = tf.keras.utils.to_categorical(y, 4)
model = keras_model.keras_model_build()
# model.summary()
opt = tf.keras.optimizers.Adam(lr=0.00001)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
hist = model.fit(x, y, batch_size=32, epochs=100, verbose=1)
model.save('model.h5')
y_pred = model.predict(x)
confusion_matrix_info(np.argmax(y, axis=1),
np.argmax(y_pred, axis=1),
title='confusion_matrix_train')
# Define traing params
#*******************************************
learning_rate = 0.00002
batch_size = 6
display_step = 1
epochs = 100
class_weights = [1, 1, 12]
generator = BalanceCovidDataset(data_dir=data_path,
csv_file=train_csv,
covid_percent=0.3,
class_weights=class_weights,
batch_size=batch_size)
total_batch = len(generator)
model = keras_model_build()
opt = tf.keras.optimizers.Adam(lr=learning_rate)
# ********************************************
# Train model
#*********************************************
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
hist = model.fit(generator,
steps_per_epoch=total_batch,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test_c),