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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/relay/testing/
H A Dlstm.py30 def lstm_cell(num_hidden, batch_size=1, dtype="float32", name=""): argument
52 input_type = relay.TensorType((batch_size, num_hidden), dtype)
53 weight_type = relay.TensorType((4 * num_hidden, num_hidden), dtype)
54 bias_type = relay.TensorType((4 * num_hidden,), dtype)
56 dense_type = relay.TensorType((batch_size, 4 * num_hidden), dtype)
79 units=num_hidden * 4,
122 def get_net(iterations, num_hidden, batch_size=1, dtype="float32"): argument
124 input_type = relay.TensorType((batch_size, num_hidden), dtype)
125 weight_type = relay.TensorType((4 * num_hidden, num_hidden), dtype)
126 bias_type = relay.TensorType((4 * num_hidden,), dtype)
[all …]
/dports/misc/tvm/incubator-tvm-0.6.1/python/tvm/relay/testing/
H A Dlstm.py29 def lstm_cell(num_hidden, batch_size=1, dtype="float32", name=""): argument
51 input_type = relay.TensorType((batch_size, num_hidden), dtype)
52 weight_type = relay.TensorType((4*num_hidden, num_hidden), dtype)
53 bias_type = relay.TensorType((4*num_hidden,), dtype)
55 dense_type = relay.TensorType((batch_size, 4*num_hidden), dtype)
74 units=num_hidden * 4,
80 units=num_hidden * 4,
118 input_type = relay.TensorType((batch_size, num_hidden), dtype)
119 weight_type = relay.TensorType((4*num_hidden, num_hidden), dtype)
120 bias_type = relay.TensorType((4*num_hidden,), dtype)
[all …]
/dports/misc/py-tvm/incubator-tvm-0.6.1/python/tvm/relay/testing/
H A Dlstm.py29 def lstm_cell(num_hidden, batch_size=1, dtype="float32", name=""): argument
51 input_type = relay.TensorType((batch_size, num_hidden), dtype)
52 weight_type = relay.TensorType((4*num_hidden, num_hidden), dtype)
53 bias_type = relay.TensorType((4*num_hidden,), dtype)
55 dense_type = relay.TensorType((batch_size, 4*num_hidden), dtype)
74 units=num_hidden * 4,
80 units=num_hidden * 4,
118 input_type = relay.TensorType((batch_size, num_hidden), dtype)
119 weight_type = relay.TensorType((4*num_hidden, num_hidden), dtype)
120 bias_type = relay.TensorType((4*num_hidden,), dtype)
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/speech_recognition/
H A Dstt_layer_fc.py24 num_hidden, argument
33 net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, no_bias=no_bias, name=name)
37 net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, no_bias=no_bias, name=name)
39 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, bias=bias, no_bias=no_bias, name=name)
42 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, no_bias=no_bias, name=…
46 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, no_bias=no_bias, name=…
48 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, bias=bias, no_bias=no_…
99 num_hidden=num_hidden_list[layer_index],
114 num_hidden=num_hidden_list[layer_index],
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/speech_recognition/
H A Dstt_layer_fc.py24 num_hidden, argument
33 net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, no_bias=no_bias, name=name)
37 net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, no_bias=no_bias, name=name)
39 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, bias=bias, no_bias=no_bias, name=name)
42 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, no_bias=no_bias, name=…
46 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, no_bias=no_bias, name=…
48 …net = mx.sym.FullyConnected(data=net, num_hidden=num_hidden, weight=weight, bias=bias, no_bias=no_…
99 num_hidden=num_hidden_list[layer_index],
114 num_hidden=num_hidden_list[layer_index],
/dports/misc/mxnet/incubator-mxnet-1.9.0/contrib/clojure-package/examples/imclassification/test/
H A Dtest-symbol.json.ref11 "attrs": {"num_hidden": "128"},
17 "attrs": {"num_hidden": "128"},
23 "attrs": {"num_hidden": "128"},
35 "attrs": {"num_hidden": "64"},
41 "attrs": {"num_hidden": "64"},
47 "attrs": {"num_hidden": "64"},
59 "attrs": {"num_hidden": "10"},
65 "attrs": {"num_hidden": "10"},
71 "attrs": {"num_hidden": "10"},
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/contrib/clojure-package/examples/imclassification/test/
H A Dtest-symbol.json.ref11 "attrs": {"num_hidden": "128"},
17 "attrs": {"num_hidden": "128"},
23 "attrs": {"num_hidden": "128"},
35 "attrs": {"num_hidden": "64"},
41 "attrs": {"num_hidden": "64"},
47 "attrs": {"num_hidden": "64"},
59 "attrs": {"num_hidden": "10"},
65 "attrs": {"num_hidden": "10"},
71 "attrs": {"num_hidden": "10"},
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/gluon/word_language_model/
H A Dmodel.py25 def __init__(self, mode, vocab_size, num_embed, num_hidden, argument
33 self.rnn = rnn.RNN(num_hidden, num_layers, dropout=dropout,
36 self.rnn = rnn.RNN(num_hidden, num_layers, 'tanh', dropout=dropout,
39 self.rnn = rnn.LSTM(num_hidden, num_layers, dropout=dropout,
42 self.rnn = rnn.GRU(num_hidden, num_layers, dropout=dropout,
49 self.decoder = nn.Dense(vocab_size, in_units=num_hidden,
52 self.decoder = nn.Dense(vocab_size, in_units=num_hidden)
54 self.num_hidden = num_hidden
60 decoded = self.decoder(output.reshape((-1, self.num_hidden)))
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/gluon/word_language_model/
H A Dmodel.py25 def __init__(self, mode, vocab_size, num_embed, num_hidden, argument
33 self.rnn = rnn.RNN(num_hidden, num_layers, dropout=dropout,
36 self.rnn = rnn.RNN(num_hidden, num_layers, 'tanh', dropout=dropout,
39 self.rnn = rnn.LSTM(num_hidden, num_layers, dropout=dropout,
42 self.rnn = rnn.GRU(num_hidden, num_layers, dropout=dropout,
49 self.decoder = nn.Dense(vocab_size, in_units=num_hidden,
52 self.decoder = nn.Dense(vocab_size, in_units=num_hidden)
54 self.num_hidden = num_hidden
60 decoded = self.decoder(output.reshape((-1, self.num_hidden)))
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/ctc/
H A Dlstm.py34 def _lstm(num_hidden, indata, prev_state, param, seqidx, layeridx): argument
39 num_hidden=num_hidden * 4,
44 num_hidden=num_hidden * 4,
58 def _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden): argument
81 num_hidden=num_hidden,
92 pred_fc = mx.sym.FullyConnected(data=hidden_concat, num_hidden=11, name="pred_fc")
129 def lstm_unroll(num_lstm_layer, seq_len, num_hidden, num_label, loss_type=None): argument
148 pred = _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden)
158 def init_states(batch_size, num_lstm_layer, num_hidden): argument
172 init_c = [('l%d_init_c' % l, (batch_size, num_hidden)) for l in range(num_lstm_layer)]
[all …]
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/ctc/
H A Dlstm.py34 def _lstm(num_hidden, indata, prev_state, param, seqidx, layeridx): argument
39 num_hidden=num_hidden * 4,
44 num_hidden=num_hidden * 4,
58 def _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden): argument
81 num_hidden=num_hidden,
92 pred_fc = mx.sym.FullyConnected(data=hidden_concat, num_hidden=11, name="pred_fc")
129 def lstm_unroll(num_lstm_layer, seq_len, num_hidden, num_label, loss_type=None): argument
148 pred = _lstm_unroll_base(num_lstm_layer, seq_len, num_hidden)
158 def init_states(batch_size, num_lstm_layer, num_hidden): argument
172 init_c = [('l%d_init_c' % l, (batch_size, num_hidden)) for l in range(num_lstm_layer)]
[all …]
/dports/misc/tvm/incubator-tvm-0.6.1/tests/python/unittest/
H A Dtest_schedule_lstm.py22 num_hidden = 1152
26 Wi2h = tvm.placeholder((4, num_hidden, num_input), name="Wi2h")
27 Wh2h = tvm.placeholder((4, num_hidden, num_hidden), name="Wh2h")
29 s_state_h = tvm.placeholder((num_step, batch_size, num_hidden))
30 s_state_c = tvm.placeholder((num_step, batch_size, num_hidden))
31 s_init_c = tvm.compute((1, batch_size, num_hidden),
33 s_init_h = tvm.compute((1, batch_size, num_hidden),
38 (num_step, 4, batch_size, num_hidden),
41 k = tvm.reduce_axis((0, num_hidden), name="ki2h")
43 (num_step, 4, batch_size, num_hidden),
[all …]
/dports/misc/py-tvm/incubator-tvm-0.6.1/tests/python/unittest/
H A Dtest_schedule_lstm.py22 num_hidden = 1152
26 Wi2h = tvm.placeholder((4, num_hidden, num_input), name="Wi2h")
27 Wh2h = tvm.placeholder((4, num_hidden, num_hidden), name="Wh2h")
29 s_state_h = tvm.placeholder((num_step, batch_size, num_hidden))
30 s_state_c = tvm.placeholder((num_step, batch_size, num_hidden))
31 s_init_c = tvm.compute((1, batch_size, num_hidden),
33 s_init_h = tvm.compute((1, batch_size, num_hidden),
38 (num_step, 4, batch_size, num_hidden),
41 k = tvm.reduce_axis((0, num_hidden), name="ki2h")
43 (num_step, 4, batch_size, num_hidden),
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/unittest/
H A Dtest_te_schedule_lstm.py24 num_hidden = 1152
28 Wi2h = te.placeholder((4, num_hidden, num_input), name="Wi2h")
29 Wh2h = te.placeholder((4, num_hidden, num_hidden), name="Wh2h")
31 s_state_h = te.placeholder((num_step, batch_size, num_hidden))
32 s_state_c = te.placeholder((num_step, batch_size, num_hidden))
33 s_init_c = te.compute((1, batch_size, num_hidden), lambda *i: 0.0, name="init_c")
34 s_init_h = te.compute((1, batch_size, num_hidden), lambda *i: 0.0, name="init_h")
38 (num_step, 4, batch_size, num_hidden),
42 k = te.reduce_axis((0, num_hidden), name="ki2h")
44 (num_step, 4, batch_size, num_hidden),
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/rnn/bucketing/
H A Dcudnn_rnn_bucketing.py106 cell.add(mx.rnn.FusedRNNCell(args.num_hidden, num_layers=1,
123 shape=(-1, args.num_hidden*(1+args.bidirectional)))
183 … cell = mx.rnn.LSTMCell(num_hidden=args.num_hidden, prefix='%s_%dl0_'%(args.rnntype,i))
187 … mx.rnn.LSTMCell(num_hidden=args.num_hidden, prefix='%s_%dr0_'%(args.rnntype,i)),
190 … cell = mx.rnn.GRUCell(num_hidden=args.num_hidden, prefix='%s_%dl0_'%(args.rnntype,i))
194 … mx.rnn.GRUCell(num_hidden=args.num_hidden, prefix='%s_%dr0_'%(args.rnntype,i)),
197 …cell = mx.rnn.RNNCell(num_hidden=args.num_hidden, activation='tanh', prefix='%s_%dl0_'%(args.rnnty…
201 … mx.rnn.RNNCell(num_hidden=args.num_hidden, activation='tanh', prefix='%s_%dr0_'%(args.rnntype,i)),
204 …cell = mx.rnn.RNNCell(num_hidden=args.num_hidden, activation='relu', prefix='%s_%dl0_'%(args.rnnty…
208 … mx.rnn.RNNCell(num_hidden=args.num_hidden, activation='relu', prefix='%s_%dr0_'%(args.rnntype,i)),
[all …]
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/rnn/bucketing/
H A Dcudnn_rnn_bucketing.py106 cell.add(mx.rnn.FusedRNNCell(args.num_hidden, num_layers=1,
123 shape=(-1, args.num_hidden*(1+args.bidirectional)))
183 … cell = mx.rnn.LSTMCell(num_hidden=args.num_hidden, prefix='%s_%dl0_'%(args.rnntype,i))
187 … mx.rnn.LSTMCell(num_hidden=args.num_hidden, prefix='%s_%dr0_'%(args.rnntype,i)),
190 … cell = mx.rnn.GRUCell(num_hidden=args.num_hidden, prefix='%s_%dl0_'%(args.rnntype,i))
194 … mx.rnn.GRUCell(num_hidden=args.num_hidden, prefix='%s_%dr0_'%(args.rnntype,i)),
197 …cell = mx.rnn.RNNCell(num_hidden=args.num_hidden, activation='tanh', prefix='%s_%dl0_'%(args.rnnty…
201 … mx.rnn.RNNCell(num_hidden=args.num_hidden, activation='tanh', prefix='%s_%dr0_'%(args.rnntype,i)),
204 …cell = mx.rnn.RNNCell(num_hidden=args.num_hidden, activation='relu', prefix='%s_%dl0_'%(args.rnnty…
208 … mx.rnn.RNNCell(num_hidden=args.num_hidden, activation='relu', prefix='%s_%dr0_'%(args.rnntype,i)),
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/rnn/old/
H A Dgru.py34 def gru(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0.): argument
46 num_hidden=num_hidden * 2,
51 num_hidden=num_hidden * 2,
62 num_hidden=num_hidden,
68 num_hidden=num_hidden,
76 num_hidden, num_embed, num_label, dropout=0.): argument
112 next_state = gru(num_hidden, indata=hidden,
124 pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=num_label,
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/rnn/old/
H A Dgru.py34 def gru(num_hidden, indata, prev_state, param, seqidx, layeridx, dropout=0.): argument
46 num_hidden=num_hidden * 2,
51 num_hidden=num_hidden * 2,
62 num_hidden=num_hidden,
68 num_hidden=num_hidden,
76 num_hidden, num_embed, num_label, dropout=0.): argument
112 next_state = gru(num_hidden, indata=hidden,
124 pred = mx.sym.FullyConnected(data=hidden_concat, num_hidden=num_label,
/dports/misc/tvm/incubator-tvm-0.6.1/topi/recipe/rnn/
H A Dlstm.py62 num_hidden = 1152 // 2
68 Wh2h = tvm.placeholder((4, num_hidden, num_hidden), name="Wh2h")
70 s_state_h = tvm.placeholder((num_step, batch_size, num_hidden))
71 s_state_c = tvm.placeholder((num_step, batch_size, num_hidden))
72 s_init_c = tvm.compute((1, batch_size, num_hidden),
74 s_init_h = tvm.compute((1, batch_size, num_hidden),
77 k = tvm.reduce_axis((0, num_hidden), name="ki2h")
79 (num_step, batch_size, 4, num_hidden),
85 gshape = (num_step, batch_size, num_hidden)
171 (num_step, batch_size, num_hidden)).astype("float32")
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/apps/topi_recipe/rnn/
H A Dlstm.py63 num_hidden = 1152 // 2
67 Xi2h = te.placeholder((num_step, batch_size, 4, num_hidden), name="Xi2h")
69 Wh2h = te.placeholder((4, num_hidden, num_hidden), name="Wh2h")
71 s_state_h = te.placeholder((num_step, batch_size, num_hidden))
72 s_state_c = te.placeholder((num_step, batch_size, num_hidden))
76 k = te.reduce_axis((0, num_hidden), name="ki2h")
78 (num_step, batch_size, 4, num_hidden),
84 gshape = (num_step, batch_size, num_hidden)
176 scan_h_np = np.zeros((num_step, batch_size, num_hidden)).astype("float32")
177 scan_c_np = np.zeros((num_step, batch_size, num_hidden)).astype("float32")
[all …]
/dports/misc/py-tvm/incubator-tvm-0.6.1/topi/recipe/rnn/
H A Dlstm.py62 num_hidden = 1152 // 2
68 Wh2h = tvm.placeholder((4, num_hidden, num_hidden), name="Wh2h")
70 s_state_h = tvm.placeholder((num_step, batch_size, num_hidden))
71 s_state_c = tvm.placeholder((num_step, batch_size, num_hidden))
72 s_init_c = tvm.compute((1, batch_size, num_hidden),
74 s_init_h = tvm.compute((1, batch_size, num_hidden),
77 k = tvm.reduce_axis((0, num_hidden), name="ki2h")
79 (num_step, batch_size, 4, num_hidden),
85 gshape = (num_step, batch_size, num_hidden)
171 (num_step, batch_size, num_hidden)).astype("float32")
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/perl-package/AI-MXNet/t/
H A Dtest_symbol.t35 my $net2 = mx->symbol->FullyConnected(name=>'fc3', num_hidden=>10);
37 $net2 = mx->symbol->FullyConnected(data=>$net2, name=>'fc4', num_hidden=>20);
133 my $num_hidden = 128;
139 my $x2h = mx->symbol->FullyConnected(data=>$data, name=>'x2h', num_hidden=>$num_hidden);
140 my $h2h = mx->symbol->FullyConnected(data=>$prev, name=>'h2h', num_hidden=>$num_hidden);
152 is_deeply($arg_shapes{x2h_weight}, [$num_hidden, $num_dim]);
160 is_deeply($arg_shapes{x2h_weight}, [$num_hidden, $num_dim]);
161 is_deeply($arg_shapes{h2h_weight}, [$num_hidden, $num_hidden]);
335 "num_hidden": "128"
380 "num_hidden": "64"
[all …]
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/perl-package/AI-MXNet/t/
H A Dtest_symbol.t35 my $net2 = mx->symbol->FullyConnected(name=>'fc3', num_hidden=>10);
37 $net2 = mx->symbol->FullyConnected(data=>$net2, name=>'fc4', num_hidden=>20);
133 my $num_hidden = 128;
139 my $x2h = mx->symbol->FullyConnected(data=>$data, name=>'x2h', num_hidden=>$num_hidden);
140 my $h2h = mx->symbol->FullyConnected(data=>$prev, name=>'h2h', num_hidden=>$num_hidden);
152 is_deeply($arg_shapes{x2h_weight}, [$num_hidden, $num_dim]);
160 is_deeply($arg_shapes{x2h_weight}, [$num_hidden, $num_dim]);
161 is_deeply($arg_shapes{h2h_weight}, [$num_hidden, $num_hidden]);
335 "num_hidden": "128"
380 "num_hidden": "64"
[all …]
/dports/misc/tvm/incubator-tvm-0.6.1/tests/python/frontend/mxnet/model_zoo/
H A Dmlp.py27 fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128, flatten=False)
29 fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64, flatten=False)
31 … fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=num_classes, flatten=False)
34 fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
36 fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
38 fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=num_classes)
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/frontend/mxnet/model_zoo/
H A Dmlp.py28 fc1 = mx.symbol.FullyConnected(data=data, name="fc1", num_hidden=128, flatten=False)
30 fc2 = mx.symbol.FullyConnected(data=act1, name="fc2", num_hidden=64, flatten=False)
32 fc3 = mx.symbol.FullyConnected(data=act2, name="fc3", num_hidden=num_classes, flatten=False)
35 fc1 = mx.symbol.FullyConnected(data=data, name="fc1", num_hidden=128)
37 fc2 = mx.symbol.FullyConnected(data=act1, name="fc2", num_hidden=64)
39 fc3 = mx.symbol.FullyConnected(data=act2, name="fc3", num_hidden=num_classes)

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