code stringlengths 3 6.57k |
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l2.theta.Transform(tf.identity) |
l1.FProp(l1_theta, conv_in, conv_pad) |
l2.FProp(l2_theta, conv_in, conv_pad) |
tf.logging.info(l1_theta) |
tf.logging.info(l2_theta) |
l1_theta.Flatten() |
l2_theta.Flatten() |
len(l1_num_vars) |
len(l2_num_var2) |
len(l1_num_vars) |
len(l2_num_var2) |
tf.global_variables_initializer() |
run() |
sess.run([conv_out1, out1_padding]) |
sess.run(w1) |
sess.run([conv_out2, out2_padding], feed_dict={w2: w1_v}) |
self.assertAllClose(v1, v2) |
self.assertAllClose(p1, p2) |
testConvBasic(self) |
testConvBnWnTanh(self) |
testConvGn(self) |
self.session(use_gpu=True) |
conv_layers_builder.Builder.Params() |
bn_layers.GroupNormLayer.Params() |
builder_params.Instantiate() |
p.Instantiate() |
tf.constant(np.random.normal(size=[4, 5, 6, 3]) |
np.full([4, 5], 0.0) |
tf.constant(conv_pad, tf.float32) |
l.FProp(l.theta, conv_in, conv_pad) |
tf.global_variables_initializer() |
run() |
sess.run(tf.reduce_sum(conv_out, 0) |
self.assertAllClose(v, expected_out) |
testConvLastWnTanh(self) |
testConvLastCausal(self) |
self.session(use_gpu=True) |
layers.DepthwiseConv2DLayer.Params() |
conv_layers_builder.Builder.Params() |
conv_layers_with_time_padding.ConvBatchNormLayer.Params() |
builder_params.Instantiate() |
p1.Instantiate() |
p2.Instantiate() |
tf.constant(np.random.normal(size=[4, 5, 6, 3]) |
np.full([4, 5], 0.0) |
tf.constant(conv_pad, tf.float32) |
l1.theta.Transform(tf.identity) |
l2.theta.Transform(tf.identity) |
l1.FProp(l1_theta, conv_in, conv_pad) |
l2.FProp(l2_theta, conv_in, conv_pad) |
tf.logging.info(l1_theta) |
tf.logging.info(l2_theta) |
l1_theta.Flatten() |
l2_theta.Flatten() |
len(l1_num_vars) |
len(l2_num_var2) |
len(l1_num_vars) |
len(l2_num_var2) |
tf.global_variables_initializer() |
run() |
sess.run([conv_out1, out1_padding]) |
sess.run([w1]) |
sess.run([conv_out2, out2_padding], feed_dict={w2: w1_v}) |
self.assertAllClose(v1, v2) |
self.assertAllClose(p1, p2) |
testDepthConvBasic(self) |
testDepthConvBnWnTanh(self) |
testDepthConvGn(self) |
self.session(use_gpu=True) |
conv_layers_builder.Builder.Params() |
bn_layers.GroupNormLayer.Params() |
builder_params.Instantiate() |
p.Instantiate() |
tf.constant(np.random.normal(size=[4, 5, 6, 4]) |
np.full([4, 5], 0.0) |
tf.constant(conv_pad, tf.float32) |
l.FProp(l.theta, conv_in, conv_pad) |
tf.global_variables_initializer() |
run() |
sess.run(tf.reduce_sum(conv_out, 0) |
self.assertAllClose(expected_out, v) |
testDepthConvLastWnTanh(self) |
testDepthConvLastCausal(self) |
self.session(use_gpu=True) |
layers.SeparableConv2DLayer.Params() |
conv_layers_builder.Builder.Params() |
conv_layers_with_time_padding.ConvBatchNormLayer.Params() |
builder_params.Instantiate() |
p1.Instantiate() |
p2.Instantiate() |
tf.constant(np.random.normal(size=[4, 5, 6, 3]) |
np.full([4, 5], 0.0) |
tf.constant(conv_pad, tf.float32) |
l1.theta.Transform(tf.identity) |
l2.theta.Transform(tf.identity) |
l1.FProp(l1_theta, conv_in, conv_pad) |
l2.FProp(l2_theta, conv_in, conv_pad) |
tf.logging.info(l1_theta) |
tf.logging.info(l2_theta) |
l1_theta.Flatten() |
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