Commit 690b5fcd authored by Pavel KORSHUNOV's avatar Pavel KORSHUNOV

corrected eval algo

parent c7404221
Pipeline #13085 passed with stages
in 9 minutes and 37 seconds
......@@ -38,18 +38,19 @@ class LSTMEval(Algorithm):
self.data_std = None
# import ipdb
# ipdb.set_trace()
features_length = input_shape[1]
if normalization_file and os.path.exists(normalization_file):
npzfile = numpy.load(normalization_file)
self.data_mean = npzfile['data_mean']
self.data_std = npzfile['data_std']
if not self.data_std.shape: # if std was saved as scalar
self.data_std = numpy.ones(input_shape)
if self.data_mean.shape[0] > input_shape[0]:
self.data_mean = self.data_mean[:input_shape[0]]
self.data_mean = numpy.reshape(self.data_mean, input_shape)
if self.data_std.shape[0] > input_shape[0]:
self.data_std = self.data_std[:input_shape[0]]
self.data_std = numpy.reshape(self.data_std, input_shape)
self.data_std = numpy.ones(features_length)
# if self.data_mean.shape[0] > input_shape[0]:
# self.data_mean = self.data_mean[:input_shape[0]]
# self.data_mean = numpy.reshape(self.data_mean, input_shape)
# if self.data_std.shape[0] > input_shape[0]:
# self.data_std = self.data_std[:input_shape[0]]
# self.data_std = numpy.reshape(self.data_std, input_shape)
else:
self.data_mean = 0
self.data_std = 1
......@@ -74,7 +75,7 @@ class LSTMEval(Algorithm):
# Creating an LSTM network
graph = lstm(inputs, lstm_cell_size, num_time_steps=num_time_steps, batch_size=batch_size,
output_activation_size=num_classes, scope='lstm',
output_activation_size=num_classes, scope='lstm', name='sync_cell',
weights_initializer=initializer, activation=tf.nn.sigmoid, reuse=reuse)
# fully connect the LSTM output to the classes
......@@ -126,6 +127,10 @@ class LSTMEval(Algorithm):
if not self.data_reader:
self.data_reader = DiskAudio([0], [0], [1] + self.input_shape)
# normalize the feature using pre-loaded normalization parameters
if self.data_std is not None and self.data_std.all() > 0:
feature = numpy.divide(feature - self.data_mean, self.data_std)
# split the feature in the sliding window frames
frames, _ = self.data_reader.split_features_in_windows(features=feature, label=1,
win_size=self.num_time_steps,
......@@ -138,9 +143,6 @@ class LSTMEval(Algorithm):
for i in range(frames.shape[0]):
frame = frames[i]
frame = numpy.reshape(frame, [1] + self.input_shape)
# normalize the frame using pre-loaded normalization parameters
if self.data_std is not None and self.data_std.all() > 0:
frame = numpy.divide(frame - self.data_mean, self.data_std)
#logger.info(" .... projecting frame of shape {0} onto DNN model".format(frame.shape))
if self.session is not None:
......
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