From f8f5750db2a82f0e6f591a76ece6034d843be8ed Mon Sep 17 00:00:00 2001 From: philipperemy Date: Tue, 13 Aug 2024 12:54:20 -0700 Subject: [PATCH] TFA deprecated --- tasks/tcn_call_test.py | 22 ---------------------- 1 file changed, 22 deletions(-) diff --git a/tasks/tcn_call_test.py b/tasks/tcn_call_test.py index e2c27fc..0f2a7dd 100644 --- a/tasks/tcn_call_test.py +++ b/tasks/tcn_call_test.py @@ -3,7 +3,6 @@ import numpy as np from tensorflow.keras import Input from tensorflow.keras import Model -from tensorflow.keras.models import Sequential from tcn import TCN @@ -100,27 +99,6 @@ def test_non_causal_time_dim_unknown_return_no_sequences(self): r = predict_with_tcn(time_steps=None, padding='same', return_sequences=False) self.assertListEqual([list(b.shape) for b in r], [[1, NB_FILTERS], [1, NB_FILTERS], [1, NB_FILTERS]]) - def test_norms(self): - Sequential(layers=[TCN(input_shape=(20, 2), use_weight_norm=True)]).compile(optimizer='adam', loss='mse') - Sequential(layers=[TCN(input_shape=(20, 2), use_weight_norm=False)]).compile(optimizer='adam', loss='mse') - Sequential(layers=[TCN(input_shape=(20, 2), use_layer_norm=True)]).compile(optimizer='adam', loss='mse') - Sequential(layers=[TCN(input_shape=(20, 2), use_layer_norm=False)]).compile(optimizer='adam', loss='mse') - Sequential(layers=[TCN(input_shape=(20, 2), use_batch_norm=True)]).compile(optimizer='adam', loss='mse') - Sequential(layers=[TCN(input_shape=(20, 2), use_batch_norm=False)]).compile(optimizer='adam', loss='mse') - try: - Sequential(layers=[TCN(input_shape=(20, 2), use_batch_norm=True, use_weight_norm=True)]).compile( - optimizer='adam', loss='mse') - raise AssertionError('test failed.') - except ValueError: - pass - try: - Sequential(layers=[TCN(input_shape=(20, 2), use_batch_norm=True, - use_weight_norm=True, use_layer_norm=True)]).compile( - optimizer='adam', loss='mse') - raise AssertionError('test failed.') - except ValueError: - pass - def test_receptive_field(self): self.assertEqual(37, TCN(kernel_size=3, dilations=(1, 3, 5), nb_stacks=1).receptive_field) self.assertEqual(379, TCN(kernel_size=4, dilations=(1, 2, 4, 8, 16, 32), nb_stacks=1).receptive_field)