from pyts.transformation import GASF, GADF
from pyts import transformation, classification, visualization
gasf = GASF(image_size=61, overlapping=False, scale='-1')
X_gasf = gasf.transform(X_standardized)
X_gasf.ndim
#3
Xt_gasf = gasf.transform(Xt_standardized)
gadf = GADF(image_size=61, overlapping=False, scale='-1')
X_gadf = gadf.transform(X_standardized)
Xt_gadf = gadf.transform(Xt_standardized)
from pyts.visualization import plot_gasf
plot_gasf(X_standardized[0], image_size=61, overlapping=False, scale='-1')
from pyts.visualization import plot_gadf
plot_gadf(X_standardized[4], image_size=30, overlapping=False, scale='-1')
x_train = X_gadf.reshape
x_train = X_gadf.reshape(X_gadf.shape[0], 61, 61, 1)
x_test = Xt_gadf.reshape(Xt_gadf.shape[0], 61, 61, 1)
#-----------------------------------------------------------------------------------------------------------------------------------------------------
def auc_roc(y_true, y_pred):
# any tensorflow metric
value, update_op = tf.contrib.metrics.streaming_auc(y_pred, y_true)
# gasf = GASF(image_size=100, overlapping=False, scale='0')
# X_gasf = gasf.transform
concerned_layers = [
"conv1d_1", "conv1d_2", "conv1d_3", "conv1d_4", "conv1d_5", "conv1d_6",
"conv1d_7", "conv1d_8"
]
for concerned_layer in concerned_layers:
print concerned_layer
layer = model.get_layer(concerned_layer)
all_filters = layer.get_weights()[0]
reorganized_filters = []
for i in range(num_filters):
print i
temp_filter = []
for filter in all_filters:
temp_filter.append(filter[0][i])
reorganized_filters.append(np.array(temp_filter))
from pyts.visualization import plot_gasf
filter_index = 0
for reorganized_filter in reorganized_filters:
plot_gasf(reorganized_filter,
image_size=kernel_size,
overlapping=False,
scale='0',
output_file="layer_%s_filter%s" %
(concerned_layer, filter_index))
filter_index += 1
print("--- train %s seconds ---" % (time.time() - start_time))