From f18532e8b83a6abba61adba07b23a0a98b573355 Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Thu, 10 Aug 2017 18:26:06 -0700 Subject: [PATCH 01/11] BasicLSTMCell --- demos/lstm/beta1_power | Bin 0 -> 4 bytes demos/lstm/beta2_power | 1 + demos/lstm/fully_connected_biases | 1 + demos/lstm/fully_connected_biases_Adam | 1 + demos/lstm/fully_connected_biases_Adam_1 | 1 + demos/lstm/fully_connected_weights | Bin 0 -> 2000 bytes demos/lstm/fully_connected_weights_Adam | Bin 0 -> 2000 bytes demos/lstm/fully_connected_weights_Adam_1 | Bin 0 -> 2000 bytes demos/lstm/index.html | 2 + demos/lstm/lstm.ts | 118 +++++++++++++++++++ demos/lstm/manifest.json | 88 ++++++++++++++ demos/lstm/rnn_basic_lstm_cell_bias | Bin 0 -> 800 bytes demos/lstm/rnn_basic_lstm_cell_bias_Adam | Bin 0 -> 800 bytes demos/lstm/rnn_basic_lstm_cell_bias_Adam_1 | Bin 0 -> 800 bytes demos/lstm/rnn_basic_lstm_cell_kernel | Bin 0 -> 48000 bytes demos/lstm/rnn_basic_lstm_cell_kernel_Adam | Bin 0 -> 48000 bytes demos/lstm/rnn_basic_lstm_cell_kernel_Adam_1 | Bin 0 -> 48000 bytes 17 files changed, 212 insertions(+) create mode 100644 demos/lstm/beta1_power create mode 100644 demos/lstm/beta2_power create mode 100644 demos/lstm/fully_connected_biases create mode 100644 demos/lstm/fully_connected_biases_Adam create mode 100644 demos/lstm/fully_connected_biases_Adam_1 create mode 100644 demos/lstm/fully_connected_weights create mode 100644 demos/lstm/fully_connected_weights_Adam create mode 100644 demos/lstm/fully_connected_weights_Adam_1 create mode 100644 demos/lstm/index.html create mode 100644 demos/lstm/lstm.ts create mode 100644 demos/lstm/manifest.json create mode 100644 demos/lstm/rnn_basic_lstm_cell_bias create mode 100644 demos/lstm/rnn_basic_lstm_cell_bias_Adam create mode 100644 demos/lstm/rnn_basic_lstm_cell_bias_Adam_1 create mode 100644 demos/lstm/rnn_basic_lstm_cell_kernel create mode 100644 demos/lstm/rnn_basic_lstm_cell_kernel_Adam create mode 100644 demos/lstm/rnn_basic_lstm_cell_kernel_Adam_1 diff --git a/demos/lstm/beta1_power b/demos/lstm/beta1_power new file mode 100644 index 0000000000000000000000000000000000000000..593f4708db84ac8fd0f5cc47c634f38c013fe9e4 GIT binary patch literal 4 LcmZQzU|;|M00aO5 literal 0 HcmV?d00001 diff --git a/demos/lstm/beta2_power b/demos/lstm/beta2_power new file mode 100644 index 0000000000..19612527cd --- /dev/null +++ b/demos/lstm/beta2_power @@ -0,0 +1 @@ +Þâ»> \ No newline at end of file diff --git a/demos/lstm/fully_connected_biases b/demos/lstm/fully_connected_biases new file mode 100644 index 0000000000..25e09a97a9 --- /dev/null +++ b/demos/lstm/fully_connected_biases @@ -0,0 +1 @@ +(lÀ>mÊP¾í€ã½¦î@¾‘D¾Lœ0¾'”q¾ê²†¾pV¾2ÞB> \ No newline at end of file diff --git a/demos/lstm/fully_connected_biases_Adam b/demos/lstm/fully_connected_biases_Adam new file mode 100644 index 0000000000..4c4ca0ec09 --- /dev/null +++ b/demos/lstm/fully_connected_biases_Adam @@ -0,0 +1 @@ +ºÊ®º Ø8ÝÝ9ªÂ©8µÌ«8b!Ò8ÖBÚ8ò„€9¦¿‰8¡ÃÑ9 \ No newline at end of file diff --git a/demos/lstm/fully_connected_biases_Adam_1 b/demos/lstm/fully_connected_biases_Adam_1 new file mode 100644 index 0000000000..0cc65d11d1 --- /dev/null +++ b/demos/lstm/fully_connected_biases_Adam_1 @@ -0,0 +1 @@ +Ưµ9S]8£€Ñ7?ß28r8Ü)°7Vã8¤ÈÕ7í¡8±Ï‘9 \ No newline at end of file diff --git a/demos/lstm/fully_connected_weights b/demos/lstm/fully_connected_weights new file mode 100644 index 0000000000000000000000000000000000000000..4f89de102e07e37dcb4330f9c48cb183770341ae GIT binary patch literal 2000 zcmWO7iCfM28UXN8C!O}aPH_s=Nu|Y9!hGoPV~%8 z3!!d3$Z{f`e(zfuEyz&X&`Mg27D5bl=b!j|5}i`e?DZ^~+2YK~_Elo@?)O2i9mkpT+7(Dad64{ew&?&S3(kZq(vx(GgDWX~XG9oW-7vmpE}^8q{ejGvB!X zux=L#h36TtN7m}NJW|M_+bm#6uaKWooJC7r5U)ISrO6x#Gr1a!14kZ7SDsNJ7_)@H zFA^HxQ;NK=E(;zs=TrGC=_FqQXD&X#PSe-8JK_%b2LXGxYB#_BZ5w8%n9{jtQM_<$ z9nAeRi)%Cu$FTQf;9>un6FX0X|E4^+cxXRb8P;NRmIA**Sxm~-4wTOUJ9kr^1+Q(# zn7|=vLf-@=RhY2)#r^o!rmO0^rWMUg4q~Sxg#2)&FHNLALz^f?$`olr>Zhf=PueV2 z87vpE+}aG#tn4pQhc#2Dvk(=3I#_Cq; zu%usp#_9VOpkDQ>98yHM_TeX(d!iNob?6w>y)dQ7C|6K+F(vV;CcJe-kv3$PW58h% z)3>ez>2fhO2$fmF#iJmVyReq^ZFuzl?yA2It1{__72Ig;Y#a_TXQ3}7=*ez?ZikdS zzHS0p$Sii!Egg5}9D&QZe?eXKI7;0nk;_SiuZ>IKYONBr9+9x5J5w0-M*$=>DpL1E zDh&2*hKRO#w5j_b=ekHpf1Vf7f#hy3M$?+!?8}nZ7!Klq=WcX&G(^P`J$A)Pi^V8? 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LSTM demo

+ diff --git a/demos/lstm/lstm.ts b/demos/lstm/lstm.ts new file mode 100644 index 0000000000..996838a87a --- /dev/null +++ b/demos/lstm/lstm.ts @@ -0,0 +1,118 @@ +/* Copyright 2017 Google Inc. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +import {Array1D, Array2D, Array3D, CheckpointLoader, Graph, NDArray, NDArrayInitializer, NDArrayMath, NDArrayMathGPU, Scalar, Session, Tensor} from '../deeplearnjs'; + +// manifest.json lives in the same directory. +const reader = new CheckpointLoader('.'); +reader.getAllVariables().then(vars => { + const input_data = [6, 0, 9, 0, 2, 5, 9, 6, 7, 0, 8, 1, 7, 9, 9, 1, 2, 4, 9]; + const math = new NDArrayMathGPU(); + const evalMethod = buildModelMathAPI(math, vars); + math.scope((keep, track) => { + let c = track(Array2D.zeros([1, 50])); + let h = track(Array2D.zeros([1, 50])); + for (var input of input_data) { + const onehot = track(Array1D.zeros([10])); + onehot.set(1.0, input); + const output = evalMethod({ + data: onehot, + c: c, + h: h, + }); + + // TODO: memory management? + console.log(output[0].get()); + output[0].dispose(); + c = output[1] as Array2D; + h = output[2] as Array2D; + } + }); +}); + +interface LSTMInput { + data: Array1D; + c: Array2D; + h: Array2D; +} + +/** + * Builds an LSTM model. + */ +function buildModelMathAPI( + math: NDArrayMath, + vars: {[varName: string]: NDArray}): (input: LSTMInput) => NDArray[] { + const fullyConnectedBiases = vars['fully_connected/biases'] as Array1D; + const fullyConnectedWeights = vars['fully_connected/weights'] as Array2D; + const lstmBias = vars['rnn/basic_lstm_cell/bias'] as Array1D; + const lstmKernel = vars['rnn/basic_lstm_cell/kernel'] as Array2D; + + return (input: LSTMInput): NDArray[] => { + return math.scope((keep, track) => { + const forget_bias = track(Scalar.new(1.0)); + + // inputs.shape = [10] + // h.shape = [1, 50] + + // concat(inputs, h, 1) + // There is no concat1d, so reshape inputs and h to 3d, concat, then reshape back to 1d. + const data3D = math.reshape(input.data, [1, 1, input.data.shape[0]]) as Array3D; + const h3D = math.reshape(input.h, [1, 1, input.h.shape[1]]) as Array3D; + const combined3D = math.concat3D(data3D, h3D, 2); + const combined2D = math.reshape(combined3D, [1, input.data.shape[0] + input.h.shape[1]]) as Array2D; + + // combined2D.shape = [1, 60] + // lstmKernel = [60, 200] + + const weighted = math.matMul(combined2D, lstmKernel); + + // weighted.shape = [1, 200] + // lstmBias.shape = [200] + + // tf.nn.bias_add(weighted, lstmBias) + // There is no broadcast add, but we can assume a batch size of 1, just reshape and do a normal add. + const weighted1D = math.reshape(weighted, [lstmBias.shape[0]]) as Array1D; + const res1D = math.add(weighted1D, lstmBias); + // Convert back to 2D so we can do slice2D operations. + const res = math.reshape(res1D, [1, res1D.shape[0]]) as Array2D; + + // i = input_gate, j = new_input, f = forget_gate, o = output_gate + const i = math.slice2D(res, [0, 0], [res.shape[0], res.shape[1] / 4]); + const j = math.slice2D(res, [0, res.shape[1] / 4 * 1], + [res.shape[0], res.shape[1] / 4]); + const f = math.slice2D(res, [0, res.shape[1] / 4 * 2], + [res.shape[0], res.shape[1] / 4]); + const o = math.slice2D(res, [0, res.shape[1] / 4 * 3], + [res.shape[0], res.shape[1] / 4]); + + const new_c = math.add( + math.elementWiseMul(input.c, math.sigmoid(math.scalarPlusArray(forget_bias, f))), + math.elementWiseMul(math.sigmoid(i), math.tanh(j))); + const new_h = math.elementWiseMul(math.tanh(new_c), math.sigmoid(o)); + + // new_h.shape = [1, 50] + + // fullyConnectedWeights.shape = [50, 10] + // fullyConnectedBiases.shape = [10] + const weightedResult = math.matMul(new_h, fullyConnectedWeights); + const weightedResult1D = math.reshape(weightedResult, [fullyConnectedBiases.shape[0]]) as Array1D; + const logits = math.add( + weightedResult1D, + fullyConnectedBiases); + + return [keep(math.argMax(logits)), keep(new_c), keep(new_h)]; + }); + }; +} diff --git a/demos/lstm/manifest.json b/demos/lstm/manifest.json new file mode 100644 index 0000000000..d885ccc4b8 --- /dev/null +++ b/demos/lstm/manifest.json @@ -0,0 +1,88 @@ +{ + "beta1_power": { + "filename": "beta1_power", + "shape": [] + }, + "beta2_power": { + "filename": "beta2_power", + "shape": [] + }, + "fully_connected/biases": { + "filename": "fully_connected_biases", + "shape": [ + 10 + ] + }, + "fully_connected/biases/Adam": { + "filename": "fully_connected_biases_Adam", + "shape": [ + 10 + ] + }, + "fully_connected/biases/Adam_1": { + "filename": "fully_connected_biases_Adam_1", + "shape": [ + 10 + ] + }, + "fully_connected/weights": { + "filename": "fully_connected_weights", + "shape": [ + 50, + 10 + ] + }, + "fully_connected/weights/Adam": { + "filename": "fully_connected_weights_Adam", + "shape": [ + 50, + 10 + ] + }, + "fully_connected/weights/Adam_1": { + "filename": "fully_connected_weights_Adam_1", + "shape": [ + 50, + 10 + ] + }, + "rnn/basic_lstm_cell/bias": { + "filename": "rnn_basic_lstm_cell_bias", + "shape": [ + 200 + ] + }, + "rnn/basic_lstm_cell/bias/Adam": { + "filename": "rnn_basic_lstm_cell_bias_Adam", + "shape": [ + 200 + ] + }, + "rnn/basic_lstm_cell/bias/Adam_1": { + "filename": "rnn_basic_lstm_cell_bias_Adam_1", + "shape": [ + 200 + ] + }, + "rnn/basic_lstm_cell/kernel": { + "filename": "rnn_basic_lstm_cell_kernel", + "shape": [ + 60, + 200 + ] + }, + "rnn/basic_lstm_cell/kernel/Adam": { + "filename": "rnn_basic_lstm_cell_kernel_Adam", + "shape": [ + 60, + 200 + ] + }, + "rnn/basic_lstm_cell/kernel/Adam_1": { + "filename": "rnn_basic_lstm_cell_kernel_Adam_1", + 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zaL+G%;yz7V2`8`@J*=t#q-W8`yS1Q|&H?e!QvV+nii1_}$JtU6-;&35EQF5z)NSwVjgU zfLhU0K1_)0e9Sb~4kX8>i*Rs?rbPR+Jv%I(KXjMJQ&6RcP?eh@=-!{p7Zu3yQanER5P$aB=)`YpUXPZuXGZQn<21A0C1%64^!{gtVz%7fJ z;B&qV9u2Psw}~NeexfNu)50Rn~Q!{MQqD;_Q)aScqwL>ma|n7^7}a0_i7#5Y+n`70=f|v5u|jr>$|E|Aa7n zUhn|2c75cgYl?isZ|_Y*=AXuoTh)mFtiTm}oaNr>{lLA)uE3AOd%5cF0(c+efes?0 zeDCcSa3EWr<{s6gok!|WN>{x9)Gfu=7Y*tDa~;w@k%31{H_?iIQgpI50(_cvu-SPZ zxy;Ze{iM@WD2b(6yG-cVo?bN9nop11=AnxF64H8B0)-Yi=r{QSENH%gP6pMu`o(N` zJt`hf)C~p`yJ}2a@D?A=J&gq$qtSlhNx0K+0D|spBZneeeChE8P3MlrNo#MyJF7F; h&;^uvd_PRyyMm&NJK3^^#>g@mk literal 0 HcmV?d00001 From 493bc5b4d6cea5ae63c4e72df6289203d5a02ec7 Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Fri, 11 Aug 2017 12:39:11 -0700 Subject: [PATCH 02/11] cleanup --- demos/lstm/index.html | 15 ++++++- demos/lstm/lstm.ts | 96 ++++++++++++++++++++++++------------------- 2 files changed, 66 insertions(+), 45 deletions(-) diff --git a/demos/lstm/index.html b/demos/lstm/index.html index 73a6d175dd..ea5f233d9a 100644 --- a/demos/lstm/index.html +++ b/demos/lstm/index.html @@ -1,2 +1,13 @@ -

LSTM demo

- + + + LSTM Demo + + + +

LSTM demo

+
Expected:
+
+
Results:
+
+ + diff --git a/demos/lstm/lstm.ts b/demos/lstm/lstm.ts index 996838a87a..512d6b39da 100644 --- a/demos/lstm/lstm.ts +++ b/demos/lstm/lstm.ts @@ -19,27 +19,31 @@ import {Array1D, Array2D, Array3D, CheckpointLoader, Graph, NDArray, NDArrayInit const reader = new CheckpointLoader('.'); reader.getAllVariables().then(vars => { const input_data = [6, 0, 9, 0, 2, 5, 9, 6, 7, 0, 8, 1, 7, 9, 9, 1, 2, 4, 9]; + const expected = [0, 9, 0, 2, 5, 9, 6, 7, 0, 8, 1, 7, 9, 9, 1, 2, 4, 9, 3]; const math = new NDArrayMathGPU(); const evalMethod = buildModelMathAPI(math, vars); + const output_data = [] as number[]; math.scope((keep, track) => { - let c = track(Array2D.zeros([1, 50])); - let h = track(Array2D.zeros([1, 50])); - for (var input of input_data) { - const onehot = track(Array1D.zeros([10])); - onehot.set(1.0, input); - const output = evalMethod({ - data: onehot, - c: c, - h: h, - }); - - // TODO: memory management? - console.log(output[0].get()); - output[0].dispose(); - c = output[1] as Array2D; - h = output[2] as Array2D; - } - }); + let c = track(Array2D.zeros([1, 50])); + let h = track(Array2D.zeros([1, 50])); + for (var input of input_data) { + const onehot = track(Array1D.zeros([10])); + onehot.set(1.0, input); + const output = evalMethod({ + data: onehot, + c: c, + h: h, + }); + + // TODO: is this memory management correct? + output_data.push(output[0].get()); + output[0].dispose(); + c = output[1] as Array2D; + h = output[2] as Array2D; + } + }); + document.getElementById('expected').innerHTML = '' + expected; + document.getElementById('results').innerHTML = '' + output_data; }); interface LSTMInput { @@ -61,32 +65,36 @@ function buildModelMathAPI( return (input: LSTMInput): NDArray[] => { return math.scope((keep, track) => { - const forget_bias = track(Scalar.new(1.0)); + const forget_bias = track(Scalar.new(1.0)); - // inputs.shape = [10] + // inputs.shape = [10] // h.shape = [1, 50] - // concat(inputs, h, 1) - // There is no concat1d, so reshape inputs and h to 3d, concat, then reshape back to 1d. - const data3D = math.reshape(input.data, [1, 1, input.data.shape[0]]) as Array3D; - const h3D = math.reshape(input.h, [1, 1, input.h.shape[1]]) as Array3D; - const combined3D = math.concat3D(data3D, h3D, 2); - const combined2D = math.reshape(combined3D, [1, input.data.shape[0] + input.h.shape[1]]) as Array2D; + // concat(inputs, h, 1) + // There is no concat1d, so reshape inputs and h to 3d, concat, then + // reshape back to 1d. + const data3D = math.reshape( + input.data, [1, 1, input.data.shape[0]]) as Array3D; + const h3D = math.reshape(input.h, [1, 1, input.h.shape[1]]) as Array3D; + const combined3D = math.concat3D(data3D, h3D, 2); + const combined2D = math.reshape( + combined3D, [1, input.data.shape[0] + input.h.shape[1]]) as Array2D; - // combined2D.shape = [1, 60] - // lstmKernel = [60, 200] + // combined2D.shape = [1, 60] + // lstmKernel = [60, 200] - const weighted = math.matMul(combined2D, lstmKernel); + const weighted = math.matMul(combined2D, lstmKernel); - // weighted.shape = [1, 200] - // lstmBias.shape = [200] + // weighted.shape = [1, 200] + // lstmBias.shape = [200] // tf.nn.bias_add(weighted, lstmBias) - // There is no broadcast add, but we can assume a batch size of 1, just reshape and do a normal add. - const weighted1D = math.reshape(weighted, [lstmBias.shape[0]]) as Array1D; + // There is no broadcast add, but we can assume a batch size of 1, + // just reshape and do a normal add. + const weighted1D = math.reshape(weighted, [lstmBias.shape[0]]) as Array1D; const res1D = math.add(weighted1D, lstmBias); - // Convert back to 2D so we can do slice2D operations. - const res = math.reshape(res1D, [1, res1D.shape[0]]) as Array2D; + // Convert back to 2D so we can do slice2D operations. + const res = math.reshape(res1D, [1, res1D.shape[0]]) as Array2D; // i = input_gate, j = new_input, f = forget_gate, o = output_gate const i = math.slice2D(res, [0, 0], [res.shape[0], res.shape[1] / 4]); @@ -98,18 +106,20 @@ function buildModelMathAPI( [res.shape[0], res.shape[1] / 4]); const new_c = math.add( - math.elementWiseMul(input.c, math.sigmoid(math.scalarPlusArray(forget_bias, f))), - math.elementWiseMul(math.sigmoid(i), math.tanh(j))); + math.elementWiseMul(input.c, + math.sigmoid(math.scalarPlusArray(forget_bias, f))), + math.elementWiseMul(math.sigmoid(i), math.tanh(j))); const new_h = math.elementWiseMul(math.tanh(new_c), math.sigmoid(o)); - // new_h.shape = [1, 50] + // new_h.shape = [1, 50] - // fullyConnectedWeights.shape = [50, 10] - // fullyConnectedBiases.shape = [10] - const weightedResult = math.matMul(new_h, fullyConnectedWeights); - const weightedResult1D = math.reshape(weightedResult, [fullyConnectedBiases.shape[0]]) as Array1D; + // fullyConnectedWeights.shape = [50, 10] + // fullyConnectedBiases.shape = [10] + const weightedResult = math.matMul(new_h, fullyConnectedWeights); + const weightedResult1D = math.reshape( + weightedResult, [fullyConnectedBiases.shape[0]]) as Array1D; const logits = math.add( - weightedResult1D, + weightedResult1D, fullyConnectedBiases); return [keep(math.argMax(logits)), keep(new_c), keep(new_h)]; From cf11b9f6df957b4a19aee456c0e05b45f34c9dac Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Fri, 11 Aug 2017 14:54:54 -0700 Subject: [PATCH 03/11] 2-layer lstm --- demos/lstm/fully_connected_biases | 2 +- demos/lstm/fully_connected_biases_Adam | 2 +- demos/lstm/fully_connected_biases_Adam_1 | 2 +- demos/lstm/fully_connected_weights | Bin 2000 -> 2000 bytes demos/lstm/fully_connected_weights_Adam | Bin 2000 -> 2000 bytes demos/lstm/fully_connected_weights_Adam_1 | Bin 2000 -> 2000 bytes demos/lstm/lstm.ts | 139 +++++++++++------- demos/lstm/manifest.json | 63 ++++++-- demos/lstm/rnn_basic_lstm_cell_bias | Bin 800 -> 0 bytes demos/lstm/rnn_basic_lstm_cell_bias_Adam | Bin 800 -> 0 bytes demos/lstm/rnn_basic_lstm_cell_bias_Adam_1 | Bin 800 -> 0 bytes demos/lstm/rnn_basic_lstm_cell_kernel | Bin 48000 -> 0 bytes demos/lstm/rnn_basic_lstm_cell_kernel_Adam | Bin 48000 -> 0 bytes demos/lstm/rnn_basic_lstm_cell_kernel_Adam_1 | Bin 48000 -> 0 bytes ...multi_rnn_cell_cell_0_basic_lstm_cell_bias | Bin 0 -> 800 bytes ..._rnn_cell_cell_0_basic_lstm_cell_bias_Adam | Bin 0 -> 800 bytes ...nn_cell_cell_0_basic_lstm_cell_bias_Adam_1 | Bin 0 -> 800 bytes ...lti_rnn_cell_cell_0_basic_lstm_cell_kernel | Bin 0 -> 48000 bytes ...nn_cell_cell_0_basic_lstm_cell_kernel_Adam | Bin 0 -> 48000 bytes ..._cell_cell_0_basic_lstm_cell_kernel_Adam_1 | Bin 0 -> 48000 bytes ...multi_rnn_cell_cell_1_basic_lstm_cell_bias | Bin 0 -> 800 bytes ..._rnn_cell_cell_1_basic_lstm_cell_bias_Adam | Bin 0 -> 800 bytes ...nn_cell_cell_1_basic_lstm_cell_bias_Adam_1 | Bin 0 -> 800 bytes ...lti_rnn_cell_cell_1_basic_lstm_cell_kernel | Bin 0 -> 80000 bytes ...nn_cell_cell_1_basic_lstm_cell_kernel_Adam | Bin 0 -> 80000 bytes ..._cell_cell_1_basic_lstm_cell_kernel_Adam_1 | Bin 0 -> 80000 bytes 26 files changed, 138 insertions(+), 70 deletions(-) delete mode 100644 demos/lstm/rnn_basic_lstm_cell_bias delete mode 100644 demos/lstm/rnn_basic_lstm_cell_bias_Adam delete mode 100644 demos/lstm/rnn_basic_lstm_cell_bias_Adam_1 delete mode 100644 demos/lstm/rnn_basic_lstm_cell_kernel delete mode 100644 demos/lstm/rnn_basic_lstm_cell_kernel_Adam delete mode 100644 demos/lstm/rnn_basic_lstm_cell_kernel_Adam_1 create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias_Adam create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias_Adam_1 create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel_Adam create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel_Adam_1 create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias_Adam create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias_Adam_1 create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel_Adam create mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel_Adam_1 diff --git a/demos/lstm/fully_connected_biases b/demos/lstm/fully_connected_biases index 25e09a97a9..0876cc96fa 100644 --- a/demos/lstm/fully_connected_biases +++ b/demos/lstm/fully_connected_biases @@ -1 +1 @@ -(lÀ>mÊP¾í€ã½¦î@¾‘D¾Lœ0¾'”q¾ê²†¾pV¾2ÞB> \ No newline at end of file +¾ߪ¾Êµ> K\¾rO¾È:¾)‰O½3ö½Qa=é*&¾ \ No newline at end of file diff --git a/demos/lstm/fully_connected_biases_Adam b/demos/lstm/fully_connected_biases_Adam index 4c4ca0ec09..60b8ed410c 100644 --- a/demos/lstm/fully_connected_biases_Adam +++ b/demos/lstm/fully_connected_biases_Adam @@ -1 +1 @@ -ºÊ®º 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reader.getAllVariables().then(vars => { - const input_data = [6, 0, 9, 0, 2, 5, 9, 6, 7, 0, 8, 1, 7, 9, 9, 1, 2, 4, 9]; - const expected = [0, 9, 0, 2, 5, 9, 6, 7, 0, 8, 1, 7, 9, 9, 1, 2, 4, 9, 3]; + const input_data = [8, 2, 8, 2, 3, 8, 8, 5, 3, 6, 0, 6, 2, 8, 8, 6, 2, 9, 6]; + const expected = [2, 8, 2, 3, 8, 8, 5, 3, 6, 0, 6, 2, 8, 8, 6, 2, 9, 6, 1]; const math = new NDArrayMathGPU(); - const evalMethod = buildModelMathAPI(math, vars); - const output_data = [] as number[]; + + const lstmKernel1 = vars[ + 'rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'] as Array2D; + const lstmBias1 = vars[ + 'rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'] as Array1D; + + const lstmKernel2 = vars[ + 'rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'] as Array2D; + const lstmBias2 = vars[ + 'rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'] as Array1D; + + const fullyConnectedBiases = vars['fully_connected/biases'] as Array1D; + const fullyConnectedWeights = vars['fully_connected/weights'] as Array2D; + + const output_data:number[] = []; + math.scope((keep, track) => { - let c = track(Array2D.zeros([1, 50])); - let h = track(Array2D.zeros([1, 50])); + const lstm1 = buildBasicLSTMCell(math, lstmKernel1, lstmBias1); + const lstm2 = buildBasicLSTMCell(math, lstmKernel2, lstmBias2); + const lstm = buildMultiRNNCell(math, [lstm1, lstm2]); + + let c = [track(Array2D.zeros([1, lstmBias1.shape[0] / 4])), + track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; + let h = [track(Array2D.zeros([1, lstmBias1.shape[0] / 4])), + track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; for (var input of input_data) { const onehot = track(Array1D.zeros([10])); onehot.set(1.0, input); - const output = evalMethod({ - data: onehot, - c: c, - h: h, - }); - - // TODO: is this memory management correct? - output_data.push(output[0].get()); - output[0].dispose(); - c = output[1] as Array2D; - h = output[2] as Array2D; + const output = lstm(onehot, c, h); + + c = output[0]; + h = output[1]; + + // TODO(fjord): need to do memory management with outputs? + + const output_h = h[1]; + const weightedResult = math.matMul(output_h, fullyConnectedWeights); + const weightedResult1D = math.reshape( + weightedResult, [fullyConnectedBiases.shape[0]]) as Array1D; + const logits = math.add( + weightedResult1D, + fullyConnectedBiases); + + output_data.push(math.argMax(logits).get()); } }); document.getElementById('expected').innerHTML = '' + expected; document.getElementById('results').innerHTML = '' + output_data; }); -interface LSTMInput { - data: Array1D; - c: Array2D; - h: Array2D; + +/** + * Builds a MultiRNNCell. + * Derived from tf.contrib.rn.MultiRNNCell. + */ +function buildMultiRNNCell(math: NDArrayMath, + basicLSTMCells: ((data: Array1D, c: Array2D, h: Array2D) => Array2D[])[]): + (data: Array1D, c: Array2D[], h: Array2D[]) => Array2D[][] { + + return (data: Array1D, c: Array2D[], h: Array2D[]): Array2D[][] => { + const res = math.scope((keep, track) => { + let input = data; + const new_states = [] + for (var i = 0; i < basicLSTMCells.length; i++) { + const output = basicLSTMCells[i](input, c[i], h[i]); + new_states.push(output[0]); + new_states.push(output[1]); + input = math.reshape(output[1], [output[1].shape[1]]) as Array1D; + } + + return new_states; + }); + const new_c:Array2D[] = []; + const new_h:Array2D[] = []; + for (var i = 0; i < res.length; i += 2) { + new_c.push(res[i] as Array2D); + new_h.push(res[i + 1] as Array2D); + } + return [new_c, new_h]; + }; } + /** - * Builds an LSTM model. + * Builds a BasicLSTMCell. + * Derived from tf.contrib.rnn.BasicLSTMCell. */ -function buildModelMathAPI( - math: NDArrayMath, - vars: {[varName: string]: NDArray}): (input: LSTMInput) => NDArray[] { - const fullyConnectedBiases = vars['fully_connected/biases'] as Array1D; - const fullyConnectedWeights = vars['fully_connected/weights'] as Array2D; - const lstmBias = vars['rnn/basic_lstm_cell/bias'] as Array1D; - const lstmKernel = vars['rnn/basic_lstm_cell/kernel'] as Array2D; +function buildBasicLSTMCell( + math: NDArrayMath, lstmKernel: Array2D, lstmBias: Array1D): + (data: Array1D, c: Array2D, h: Array2D) => Array2D[] { - return (input: LSTMInput): NDArray[] => { + return (data: Array1D, c: Array2D, h: Array2D): Array2D[] => { return math.scope((keep, track) => { const forget_bias = track(Scalar.new(1.0)); - // inputs.shape = [10] - // h.shape = [1, 50] - // concat(inputs, h, 1) // There is no concat1d, so reshape inputs and h to 3d, concat, then // reshape back to 1d. const data3D = math.reshape( - input.data, [1, 1, input.data.shape[0]]) as Array3D; - const h3D = math.reshape(input.h, [1, 1, input.h.shape[1]]) as Array3D; + data, [1, 1, data.shape[0]]) as Array3D; + const h3D = math.reshape(h, [1, 1, h.shape[1]]) as Array3D; const combined3D = math.concat3D(data3D, h3D, 2); const combined2D = math.reshape( - combined3D, [1, input.data.shape[0] + input.h.shape[1]]) as Array2D; - - // combined2D.shape = [1, 60] - // lstmKernel = [60, 200] + combined3D, [1, data.shape[0] + h.shape[1]]) as Array2D; const weighted = math.matMul(combined2D, lstmKernel); - // weighted.shape = [1, 200] - // lstmBias.shape = [200] - // tf.nn.bias_add(weighted, lstmBias) // There is no broadcast add, but we can assume a batch size of 1, // just reshape and do a normal add. @@ -106,23 +146,12 @@ function buildModelMathAPI( [res.shape[0], res.shape[1] / 4]); const new_c = math.add( - math.elementWiseMul(input.c, + math.elementWiseMul(c, math.sigmoid(math.scalarPlusArray(forget_bias, f))), math.elementWiseMul(math.sigmoid(i), math.tanh(j))); const new_h = math.elementWiseMul(math.tanh(new_c), math.sigmoid(o)); - // new_h.shape = [1, 50] - - // fullyConnectedWeights.shape = [50, 10] - // fullyConnectedBiases.shape = [10] - const weightedResult = math.matMul(new_h, fullyConnectedWeights); - const weightedResult1D = math.reshape( - weightedResult, [fullyConnectedBiases.shape[0]]) as Array1D; - const logits = math.add( - weightedResult1D, - fullyConnectedBiases); - - return [keep(math.argMax(logits)), keep(new_c), keep(new_h)]; + return [keep(new_c), keep(new_h)]; }); }; } diff --git a/demos/lstm/manifest.json b/demos/lstm/manifest.json index d885ccc4b8..b7dd2be0f9 100644 --- a/demos/lstm/manifest.json +++ b/demos/lstm/manifest.json @@ -46,43 +46,82 @@ 10 ] }, - "rnn/basic_lstm_cell/bias": { - "filename": "rnn_basic_lstm_cell_bias", + "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias": { + "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias", "shape": [ 200 ] }, - "rnn/basic_lstm_cell/bias/Adam": { - "filename": "rnn_basic_lstm_cell_bias_Adam", + "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias/Adam": { + "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias_Adam", "shape": [ 200 ] }, - "rnn/basic_lstm_cell/bias/Adam_1": { - "filename": "rnn_basic_lstm_cell_bias_Adam_1", + "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias/Adam_1": { + "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias_Adam_1", "shape": [ 200 ] }, - "rnn/basic_lstm_cell/kernel": { - "filename": "rnn_basic_lstm_cell_kernel", + "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel": { + "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel", "shape": [ 60, 200 ] }, - "rnn/basic_lstm_cell/kernel/Adam": { - "filename": "rnn_basic_lstm_cell_kernel_Adam", + "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel/Adam": { + "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel_Adam", "shape": [ 60, 200 ] }, - "rnn/basic_lstm_cell/kernel/Adam_1": { - "filename": "rnn_basic_lstm_cell_kernel_Adam_1", + "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel/Adam_1": { + "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel_Adam_1", "shape": [ 60, 200 ] + }, + "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias": { + "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias", + "shape": [ + 200 + ] + }, + "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias/Adam": { + "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias_Adam", + "shape": [ + 200 + ] + }, + "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias/Adam_1": { + "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias_Adam_1", + "shape": [ + 200 + ] + }, + "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel": { + "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel", + "shape": [ + 100, + 200 + ] + }, + "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel/Adam": { + "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel_Adam", + "shape": [ + 100, + 200 + ] + }, + "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel/Adam_1": { + "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel_Adam_1", + "shape": [ + 100, + 200 + ] } } \ No newline at end of file diff --git a/demos/lstm/rnn_basic_lstm_cell_bias b/demos/lstm/rnn_basic_lstm_cell_bias deleted file mode 100644 index 4e57de8bbef087ac819e309b2e253d66522e60b8..0000000000000000000000000000000000000000 GIT binary patch literal 0 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HcmV?d00001 From 0c9d50cfac1ad626fe10faee75158bd997db0ccc Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Fri, 11 Aug 2017 15:15:08 -0700 Subject: [PATCH 04/11] add training script for pi digits --- demos/lstm/fully_connected_biases | 2 +- demos/lstm/fully_connected_biases_Adam | 2 +- demos/lstm/fully_connected_biases_Adam_1 | 2 +- demos/lstm/fully_connected_weights | Bin 2000 -> 2000 bytes demos/lstm/fully_connected_weights_Adam | Bin 2000 -> 2000 bytes demos/lstm/fully_connected_weights_Adam_1 | Bin 2000 -> 2000 bytes demos/lstm/lstm.ts | 4 +- ...multi_rnn_cell_cell_0_basic_lstm_cell_bias | Bin 800 -> 800 bytes ..._rnn_cell_cell_0_basic_lstm_cell_bias_Adam | Bin 800 -> 800 bytes ...nn_cell_cell_0_basic_lstm_cell_bias_Adam_1 | Bin 800 -> 800 bytes ...lti_rnn_cell_cell_0_basic_lstm_cell_kernel | Bin 48000 -> 48000 bytes ...nn_cell_cell_0_basic_lstm_cell_kernel_Adam | Bin 48000 -> 48000 bytes ..._cell_cell_0_basic_lstm_cell_kernel_Adam_1 | Bin 48000 -> 48000 bytes ...multi_rnn_cell_cell_1_basic_lstm_cell_bias | Bin 800 -> 800 bytes ..._rnn_cell_cell_1_basic_lstm_cell_bias_Adam | Bin 800 -> 800 bytes ...nn_cell_cell_1_basic_lstm_cell_bias_Adam_1 | Bin 800 -> 800 bytes ...lti_rnn_cell_cell_1_basic_lstm_cell_kernel | Bin 80000 -> 80000 bytes ...nn_cell_cell_1_basic_lstm_cell_kernel_Adam | Bin 80000 -> 80000 bytes ..._cell_cell_1_basic_lstm_cell_kernel_Adam_1 | Bin 80000 -> 80000 bytes demos/lstm/train.py | 84 ++++++++++++++++++ 20 files changed, 89 insertions(+), 5 deletions(-) create mode 100644 demos/lstm/train.py diff --git a/demos/lstm/fully_connected_biases b/demos/lstm/fully_connected_biases index 0876cc96fa..b5074b3cac 100644 --- a/demos/lstm/fully_connected_biases +++ b/demos/lstm/fully_connected_biases @@ -1 +1 @@ -¾ߪ¾Êµ> K\¾rO¾È:¾)‰O½3ö½Qa=é*&¾ \ No newline at end of file +¬“¾6=Ñ>£}b¾/­æ½px“=IÕL½³]1¾ØÓ)¾'˜¾³|½ \ No newline at end of file diff --git a/demos/lstm/fully_connected_biases_Adam b/demos/lstm/fully_connected_biases_Adam index 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a/demos/lstm/train.py b/demos/lstm/train.py new file mode 100644 index 0000000000..2e3c76ae1e --- /dev/null +++ b/demos/lstm/train.py @@ -0,0 +1,84 @@ +# Copyright 2017 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Trains and Evaluates a simple LSTM network.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +FLAGS = tf.app.flags.FLAGS + +tf.app.flags.DEFINE_string('output_dir', '/tmp/simple_lstm', + 'Directory to write checkpoint.') + +def main(unused_argv): + data = np.array( + [[3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8, 9, 7, 9, 3, 2, 3, 8, 4]]) + + tf.reset_default_graph() + + x = tf.placeholder(dtype=tf.int32, shape=[1, 19]) + y = tf.placeholder(dtype=tf.int32, shape=[1, 19]) + + NHIDDEN = 50 + NLABELS = 10 + + lstm1 = tf.contrib.rnn.BasicLSTMCell(NHIDDEN) + lstm2 = tf.contrib.rnn.BasicLSTMCell(NHIDDEN) + lstm = tf.contrib.rnn.MultiRNNCell([lstm1, lstm2]) + initial_state = lstm.zero_state(1, tf.float32) + + outputs, final_state = tf.nn.dynamic_rnn( + cell=lstm, inputs=tf.one_hot(x, NLABELS), initial_state=initial_state) + + logits = tf.contrib.layers.linear(outputs, NLABELS) + + softmax_cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( + labels=y, logits=logits) + + predictions = tf.argmax(logits, axis=-1) + loss = tf.reduce_mean(softmax_cross_entropy) + + train_op = tf.train.AdamOptimizer().minimize(loss) + + sess = tf.InteractiveSession() + sess.run(tf.global_variables_initializer()) + + print('Starting training...') + NEPOCH = 1000 + for step in range(NEPOCH + 1): + loss_out, _ = sess.run([loss, train_op], + feed_dict={ + x: data[:, :-1], + y: data[:, 1:], + }) + if step % 100 == 0: + print('Loss at step {}: {}'.format(step, loss_out)) + + print('Expected data:') + print(data[:, 1:]) + print('Results:') + print(sess.run([predictions], feed_dict={x: data[:, :-1]})) + + saver = tf.train.Saver() + path = saver.save(sess, FLAGS.output_dir, global_step=step) + print('Saved checkpoint at {}'.format(path)) + + +if __name__ == '__main__': + tf.app.run(main) + From 1bf4841803cf45a8ee65d7344dce1412dac5716f Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Mon, 14 Aug 2017 15:54:01 -0700 Subject: [PATCH 05/11] start --- demos/lstm/lstm.ts | 96 +++------------------------------------------- src/math/math.ts | 92 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 97 insertions(+), 91 deletions(-) diff --git a/demos/lstm/lstm.ts b/demos/lstm/lstm.ts index 891bbf8766..decc61e0e0 100644 --- a/demos/lstm/lstm.ts +++ b/demos/lstm/lstm.ts @@ -38,9 +38,9 @@ reader.getAllVariables().then(vars => { const output_data:number[] = []; math.scope((keep, track) => { - const lstm1 = buildBasicLSTMCell(math, lstmKernel1, lstmBias1); - const lstm2 = buildBasicLSTMCell(math, lstmKernel2, lstmBias2); - const lstm = buildMultiRNNCell(math, [lstm1, lstm2]); + const forgetBias = track(Scalar.new(1.0)); + const lstm1 = math.basicLSTMCell.bind(forgetBias, lstmKernel1, lstmBias1); + const lstm2 = math.basicLSTMCell.bind(forgetBias, lstmKernel2, lstmBias2); let c = [track(Array2D.zeros([1, lstmBias1.shape[0] / 4])), track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; @@ -49,13 +49,12 @@ reader.getAllVariables().then(vars => { for (var input of input_data) { const onehot = track(Array1D.zeros([10])); onehot.set(1.0, input); - const output = lstm(onehot, c, h); + + const output = math.multiRNNCell([lstm1, lstm2], onehot, c, h); c = output[0]; h = output[1]; - // TODO(fjord): need to do memory management with outputs? - const output_h = h[1]; const weightedResult = math.matMul(output_h, fullyConnectedWeights); const weightedResult1D = math.reshape( @@ -70,88 +69,3 @@ reader.getAllVariables().then(vars => { document.getElementById('expected').innerHTML = '' + expected; document.getElementById('results').innerHTML = '' + output_data; }); - - -/** - * Builds a MultiRNNCell. - * Derived from tf.contrib.rn.MultiRNNCell. - */ -function buildMultiRNNCell(math: NDArrayMath, - basicLSTMCells: ((data: Array1D, c: Array2D, h: Array2D) => Array2D[])[]): - (data: Array1D, c: Array2D[], h: Array2D[]) => Array2D[][] { - - return (data: Array1D, c: Array2D[], h: Array2D[]): Array2D[][] => { - const res = math.scope((keep, track) => { - let input = data; - const new_states = [] - for (var i = 0; i < basicLSTMCells.length; i++) { - const output = basicLSTMCells[i](input, c[i], h[i]); - new_states.push(output[0]); - new_states.push(output[1]); - input = math.reshape(output[1], [output[1].shape[1]]) as Array1D; - } - - return new_states; - }); - const new_c:Array2D[] = []; - const new_h:Array2D[] = []; - for (var i = 0; i < res.length; i += 2) { - new_c.push(res[i] as Array2D); - new_h.push(res[i + 1] as Array2D); - } - return [new_c, new_h]; - }; -} - - -/** - * Builds a BasicLSTMCell. - * Derived from tf.contrib.rnn.BasicLSTMCell. - */ -function buildBasicLSTMCell( - math: NDArrayMath, lstmKernel: Array2D, lstmBias: Array1D): - (data: Array1D, c: Array2D, h: Array2D) => Array2D[] { - - return (data: Array1D, c: Array2D, h: Array2D): Array2D[] => { - return math.scope((keep, track) => { - const forget_bias = track(Scalar.new(1.0)); - - // concat(inputs, h, 1) - // There is no concat1d, so reshape inputs and h to 3d, concat, then - // reshape back to 1d. - const data3D = math.reshape( - data, [1, 1, data.shape[0]]) as Array3D; - const h3D = math.reshape(h, [1, 1, h.shape[1]]) as Array3D; - const combined3D = math.concat3D(data3D, h3D, 2); - const combined2D = math.reshape( - combined3D, [1, data.shape[0] + h.shape[1]]) as Array2D; - - const weighted = math.matMul(combined2D, lstmKernel); - - // tf.nn.bias_add(weighted, lstmBias) - // There is no broadcast add, but we can assume a batch size of 1, - // just reshape and do a normal add. - const weighted1D = math.reshape(weighted, [lstmBias.shape[0]]) as Array1D; - const res1D = math.add(weighted1D, lstmBias); - // Convert back to 2D so we can do slice2D operations. - const res = math.reshape(res1D, [1, res1D.shape[0]]) as Array2D; - - // i = input_gate, j = new_input, f = forget_gate, o = output_gate - const i = math.slice2D(res, [0, 0], [res.shape[0], res.shape[1] / 4]); - const j = math.slice2D(res, [0, res.shape[1] / 4 * 1], - [res.shape[0], res.shape[1] / 4]); - const f = math.slice2D(res, [0, res.shape[1] / 4 * 2], - [res.shape[0], res.shape[1] / 4]); - const o = math.slice2D(res, [0, res.shape[1] / 4 * 3], - [res.shape[0], res.shape[1] / 4]); - - const new_c = math.add( - math.elementWiseMul(c, - math.sigmoid(math.scalarPlusArray(forget_bias, f))), - math.elementWiseMul(math.sigmoid(i), math.tanh(j))); - const new_h = math.elementWiseMul(math.tanh(new_c), math.sigmoid(o)); - - return [keep(new_c), keep(new_h)]; - }); - }; -} diff --git a/src/math/math.ts b/src/math/math.ts index 8d693a570e..ce7564c6e9 100644 --- a/src/math/math.ts +++ b/src/math/math.ts @@ -21,6 +21,13 @@ import {Array1D, Array2D, Array3D, Array4D, NDArray, Scalar} from './ndarray'; export type ScopeResult = NDArray[]|NDArray|void; +/* +export interface LSTMCell { + (data: Array1D, c: Array2D, h: Array2D): Array2D[]; +}; +*/ + + export abstract class NDArrayMath { private ndarrayScopes: NDArray[][] = []; private activeScope: NDArray[]; @@ -1127,6 +1134,91 @@ export abstract class NDArrayMath { x: Array3D, mean: Array3D|Array1D, variance: Array3D|Array1D, varianceEpsilon: number, scale?: Array3D|Array1D, offset?: Array3D|Array1D): Array3D; + + ////////////// + // LSTM ops // + ////////////// + + /** + * Builds a MultiRNNCell. + * Derived from tf.contrib.rn.MultiRNNCell. + */ + multiRNNCell(lstmCells: ((data: Array1D, c: Array2D, h: Array2D) => Array2D[])[], data: Array1D, c: Array2D[], + h: Array2D[]): Array2D[][] { + const res = this.scope((keep, track) => { + let input = data; + const new_states = [] + for (let i = 0; i < lstmCells.length; i++) { + const output = lstmCells[i](input, c[i], h[i]); + new_states.push(output[0]); + new_states.push(output[1]); + input = this.reshape(output[1], [output[1].shape[1]]) as Array1D; + } + + return new_states; + }); + const new_c:Array2D[] = []; + const new_h:Array2D[] = []; + for (let i = 0; i < res.length; i += 2) { + new_c.push(res[i] as Array2D); + new_h.push(res[i + 1] as Array2D); + } + return [new_c, new_h]; + } + + + /** + * Computes the next state and output of a BasicLSTMCell. + * Derived from tf.contrib.rnn.BasicLSTMCell. + * @param forgetBias Forget bias for the cell. + * @param lstmKernel The weights for the cell. + * @param lstmBias The biases for the cell. + * @param data The input to the cell. + * @param c Previous cell state. + * @param h Previous cell output. + */ + basicLSTMCell(forgetBias: Scalar, lstmKernel: Array2D, lstmBias: Array1D, + data: Array1D, c: Array2D, h: Array2D): Array2D[] { + return this.scope((keep, track) => { + // concat(inputs, h, 1) + // There is no concat1d, so reshape inputs and h to 3d, concat, then + // reshape back to 1d. + const data3D = this.reshape( + data, [1, 1, data.shape[0]]) as Array3D; + const h3D = this.reshape(h, [1, 1, h.shape[1]]) as Array3D; + const combined3D = this.concat3D(data3D, h3D, 2); + const combined2D = this.reshape( + combined3D, [1, data.shape[0] + h.shape[1]]) as Array2D; + + const weighted = this.matMul(combined2D, lstmKernel); + + // tf.nn.bias_add(weighted, lstmBias) + // There is no broadcast add, but we can assume a batch size of 1, + // just reshape and do a normal add. + const weighted1D = this.reshape(weighted, [lstmBias.shape[0]]) as Array1D; + const res1D = this.add(weighted1D, lstmBias); + // Convert back to 2D so we can do slice2D operations. + const res = this.reshape(res1D, [1, res1D.shape[0]]) as Array2D; + + // i = input_gate, j = new_input, f = forget_gate, o = output_gate + const i = this.slice2D(res, [0, 0], [res.shape[0], res.shape[1] / 4]); + const j = this.slice2D(res, [0, res.shape[1] / 4 * 1], + [res.shape[0], res.shape[1] / 4]); + const f = this.slice2D(res, [0, res.shape[1] / 4 * 2], + [res.shape[0], res.shape[1] / 4]); + const o = this.slice2D(res, [0, res.shape[1] / 4 * 3], + [res.shape[0], res.shape[1] / 4]); + + const new_c = this.add( + this.elementWiseMul(c, + this.sigmoid(this.scalarPlusArray(forgetBias, f))), + this.elementWiseMul(this.sigmoid(i), this.tanh(j))); + const new_h = this.elementWiseMul(this.tanh(new_c), this.sigmoid(o)); + + return [new_c, new_h]; + }); + } + } export enum MatrixOrientation { From 1640f644b1c732bbad683beef56465ff2541ca85 Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Mon, 14 Aug 2017 16:13:19 -0700 Subject: [PATCH 06/11] interface --- demos/lstm/lstm.ts | 6 +++--- src/math/math.ts | 17 ++++++++++------- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/demos/lstm/lstm.ts b/demos/lstm/lstm.ts index decc61e0e0..309dc966be 100644 --- a/demos/lstm/lstm.ts +++ b/demos/lstm/lstm.ts @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -import {Array1D, Array2D, Array3D, CheckpointLoader, Graph, NDArray, NDArrayInitializer, NDArrayMath, NDArrayMathGPU, Scalar, Session, Tensor} from '../deeplearnjs'; +import {Array1D, Array2D, CheckpointLoader, NDArrayMathGPU, Scalar} from '../deeplearnjs'; // manifest.json lives in the same directory. const reader = new CheckpointLoader('.'); @@ -39,8 +39,8 @@ reader.getAllVariables().then(vars => { math.scope((keep, track) => { const forgetBias = track(Scalar.new(1.0)); - const lstm1 = math.basicLSTMCell.bind(forgetBias, lstmKernel1, lstmBias1); - const lstm2 = math.basicLSTMCell.bind(forgetBias, lstmKernel2, lstmBias2); + const lstm1 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel1, lstmBias1); + const lstm2 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel2, lstmBias2); let c = [track(Array2D.zeros([1, lstmBias1.shape[0] / 4])), track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; diff --git a/src/math/math.ts b/src/math/math.ts index ce7564c6e9..b6b5603017 100644 --- a/src/math/math.ts +++ b/src/math/math.ts @@ -21,11 +21,9 @@ import {Array1D, Array2D, Array3D, Array4D, NDArray, Scalar} from './ndarray'; export type ScopeResult = NDArray[]|NDArray|void; -/* export interface LSTMCell { (data: Array1D, c: Array2D, h: Array2D): Array2D[]; }; -*/ export abstract class NDArrayMath { @@ -1140,10 +1138,15 @@ export abstract class NDArrayMath { ////////////// /** - * Builds a MultiRNNCell. + * Computes the next states and outputs of a stack of LSTMCells. + * Each cell output is used as input to the next cell. * Derived from tf.contrib.rn.MultiRNNCell. + * @param lstmCells Array of LSTMCell functions. + * @param data The input to the cell. + * @param c Array of previous cell states. + * @param h Array of previous cell outputs. */ - multiRNNCell(lstmCells: ((data: Array1D, c: Array2D, h: Array2D) => Array2D[])[], data: Array1D, c: Array2D[], + multiRNNCell(lstmCells: LSTMCell[], data: Array1D, c: Array2D[], h: Array2D[]): Array2D[][] { const res = this.scope((keep, track) => { let input = data; @@ -1166,7 +1169,6 @@ export abstract class NDArrayMath { return [new_c, new_h]; } - /** * Computes the next state and output of a BasicLSTMCell. * Derived from tf.contrib.rnn.BasicLSTMCell. @@ -1212,8 +1214,9 @@ export abstract class NDArrayMath { const new_c = this.add( this.elementWiseMul(c, this.sigmoid(this.scalarPlusArray(forgetBias, f))), - this.elementWiseMul(this.sigmoid(i), this.tanh(j))); - const new_h = this.elementWiseMul(this.tanh(new_c), this.sigmoid(o)); + this.elementWiseMul(this.sigmoid(i), this.tanh(j))) as Array2D; + const new_h = this.elementWiseMul( + this.tanh(new_c), this.sigmoid(o)) as Array2D; return [new_c, new_h]; }); From 20e5597be216bf24fa9862ca83e39d89d6934b1a Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Mon, 14 Aug 2017 16:59:45 -0700 Subject: [PATCH 07/11] docs --- demos/lstm/README.md | 25 +++++ demos/lstm/beta1_power | Bin 4 -> 0 bytes demos/lstm/beta2_power | 1 - demos/lstm/fully_connected_biases | 2 +- demos/lstm/fully_connected_biases_Adam | 1 - demos/lstm/fully_connected_biases_Adam_1 | 1 - demos/lstm/fully_connected_weights | Bin 2000 -> 800 bytes demos/lstm/fully_connected_weights_Adam | Bin 2000 -> 0 bytes demos/lstm/fully_connected_weights_Adam_1 | Bin 2000 -> 0 bytes demos/lstm/index.html | 1 + demos/lstm/lstm.ts | 31 +++++- demos/lstm/manifest.json | 100 ++---------------- ...multi_rnn_cell_cell_0_basic_lstm_cell_bias | Bin 800 -> 320 bytes ..._rnn_cell_cell_0_basic_lstm_cell_bias_Adam | Bin 800 -> 0 bytes ...nn_cell_cell_0_basic_lstm_cell_bias_Adam_1 | Bin 800 -> 0 bytes ...lti_rnn_cell_cell_0_basic_lstm_cell_kernel | Bin 48000 -> 9600 bytes ...nn_cell_cell_0_basic_lstm_cell_kernel_Adam | Bin 48000 -> 0 bytes ..._cell_cell_0_basic_lstm_cell_kernel_Adam_1 | Bin 48000 -> 0 bytes ...multi_rnn_cell_cell_1_basic_lstm_cell_bias | Bin 800 -> 320 bytes ..._rnn_cell_cell_1_basic_lstm_cell_bias_Adam | 2 - ...nn_cell_cell_1_basic_lstm_cell_bias_Adam_1 | Bin 800 -> 0 bytes ...lti_rnn_cell_cell_1_basic_lstm_cell_kernel | Bin 80000 -> 12800 bytes ...nn_cell_cell_1_basic_lstm_cell_kernel_Adam | Bin 80000 -> 0 bytes ..._cell_cell_1_basic_lstm_cell_kernel_Adam_1 | Bin 80000 -> 0 bytes demos/lstm/train.py | 6 +- src/math/math_gpu_test.ts | 59 +++++++++++ 26 files changed, 122 insertions(+), 107 deletions(-) create mode 100644 demos/lstm/README.md delete mode 100644 demos/lstm/beta1_power delete mode 100644 demos/lstm/beta2_power delete mode 100644 demos/lstm/fully_connected_biases_Adam delete mode 100644 demos/lstm/fully_connected_biases_Adam_1 delete mode 100644 demos/lstm/fully_connected_weights_Adam delete mode 100644 demos/lstm/fully_connected_weights_Adam_1 delete mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias_Adam delete mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias_Adam_1 delete mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel_Adam delete mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel_Adam_1 delete mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias_Adam delete mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias_Adam_1 delete mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel_Adam delete mode 100644 demos/lstm/rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel_Adam_1 diff --git a/demos/lstm/README.md b/demos/lstm/README.md new file mode 100644 index 0000000000..9dd532eebf --- /dev/null +++ b/demos/lstm/README.md @@ -0,0 +1,25 @@ +# LSTM Demo + +Demonstrates training a simple autoregressive LSTM network in Tensorflow and +then porting that model to deeplearn.js. + +This network uses two BasicLSTMCells combined with MultiRNNCell. The network is +trained the memorize the first few digits of pi. + +First, train the LSTM network with Tensorflow: + +``` +python demos/lstm/train.py +``` + +Next, export the weights to be used by deeplearn.js.: + +``` +python scripts/dump_checkpoint_vars.py --output_dir=demos/lstm/ --checkpoint_file=/tmp/simple_lstm-1000 --remove_variables_regex=".*Adam.*|.*beta.*" +``` + +Finally, start the demo: + +``` +scripts/watch-demo demos/lstm/lstm.ts +``` diff --git a/demos/lstm/beta1_power b/demos/lstm/beta1_power deleted file mode 100644 index 593f4708db84ac8fd0f5cc47c634f38c013fe9e4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4 LcmZQzU|;|M00aO5 diff --git a/demos/lstm/beta2_power b/demos/lstm/beta2_power deleted file mode 100644 index 19612527cd..0000000000 --- a/demos/lstm/beta2_power +++ /dev/null @@ -1 +0,0 @@ -Þâ»> \ No newline at end of file diff --git a/demos/lstm/fully_connected_biases b/demos/lstm/fully_connected_biases index b5074b3cac..9f8fb8cdf4 100644 --- a/demos/lstm/fully_connected_biases +++ b/demos/lstm/fully_connected_biases @@ -1 +1 @@ -¬“¾6=Ñ>£}b¾/­æ½px“=IÕL½³]1¾ØÓ)¾'˜¾³|½ \ No newline at end of file +,:M¾Pþ>Ƴ¾mZK½fN<>úÖ»ó¯G¾d޾ò²6¾óH¾ \ No newline at end of file diff --git a/demos/lstm/fully_connected_biases_Adam b/demos/lstm/fully_connected_biases_Adam deleted file mode 100644 index 957c4776ef..0000000000 --- a/demos/lstm/fully_connected_biases_Adam +++ /dev/null @@ -1 +0,0 @@ -7tè7u. ºìH·ù(ö71›×9¦„8q>·¸ùÄ7W£·Õ”Ü7 \ No newline at end of file diff --git a/demos/lstm/fully_connected_biases_Adam_1 b/demos/lstm/fully_connected_biases_Adam_1 deleted file mode 100644 index b4be2c9c50..0000000000 --- a/demos/lstm/fully_connected_biases_Adam_1 +++ /dev/null @@ -1 +0,0 @@ -z³8ïçÇ9þ‘´7HÌ,8¯þ±8á¼'8x8¨Åâ7€¢78 \ No newline at end of file diff --git a/demos/lstm/fully_connected_weights b/demos/lstm/fully_connected_weights index 739613eaeb766dd0d50b2dc0736ff21ede71ea39..8061f0c24ff714d312626d37b7f3f1025bd59d9e 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LSTM demo

Results:
+
diff --git a/demos/lstm/lstm.ts b/demos/lstm/lstm.ts index 309dc966be..10eba9b65f 100644 --- a/demos/lstm/lstm.ts +++ b/demos/lstm/lstm.ts @@ -18,7 +18,7 @@ import {Array1D, Array2D, CheckpointLoader, NDArrayMathGPU, Scalar} from '../dee // manifest.json lives in the same directory. const reader = new CheckpointLoader('.'); reader.getAllVariables().then(vars => { - const input_data = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8, 9, 7, 9, 3, 2, 3, 8]; + const primer_data = 3; const expected = [1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8, 9, 7, 9, 3, 2, 3, 8, 4]; const math = new NDArrayMathGPU(); @@ -35,7 +35,7 @@ reader.getAllVariables().then(vars => { const fullyConnectedBiases = vars['fully_connected/biases'] as Array1D; const fullyConnectedWeights = vars['fully_connected/weights'] as Array2D; - const output_data:number[] = []; + const results:number[] = []; math.scope((keep, track) => { const forgetBias = track(Scalar.new(1.0)); @@ -46,7 +46,9 @@ reader.getAllVariables().then(vars => { track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; let h = [track(Array2D.zeros([1, lstmBias1.shape[0] / 4])), track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; - for (var input of input_data) { + + let input = primer_data; + for (let i = 0; i < expected.length; i++) { const onehot = track(Array1D.zeros([10])); onehot.set(1.0, input); @@ -63,9 +65,28 @@ reader.getAllVariables().then(vars => { weightedResult1D, fullyConnectedBiases); - output_data.push(math.argMax(logits).get()); + const result = math.argMax(logits).get(); + results.push(result); + input = result; } }); document.getElementById('expected').innerHTML = '' + expected; - document.getElementById('results').innerHTML = '' + output_data; + document.getElementById('results').innerHTML = '' + results; + if(checkArrays(expected, results)) { + document.getElementById('success').innerHTML = 'Success!'; + } else { + document.getElementById('success').innerHTML = 'Failure.'; + } }); + +function checkArrays(expected: number[], results: number[]) { + if(expected.length != results.length) { + return false; + } + for(let i = 0; i < expected.length; i++) { + if(expected[i] != results[i]) { + return false; + } + } + return true; +} diff --git a/demos/lstm/manifest.json b/demos/lstm/manifest.json index b7dd2be0f9..13a1f25732 100644 --- a/demos/lstm/manifest.json +++ b/demos/lstm/manifest.json @@ -1,127 +1,41 @@ { - "beta1_power": { - "filename": "beta1_power", - "shape": [] - }, - "beta2_power": { - "filename": "beta2_power", - "shape": [] - }, "fully_connected/biases": { "filename": "fully_connected_biases", "shape": [ 10 ] }, - "fully_connected/biases/Adam": { - "filename": "fully_connected_biases_Adam", - "shape": [ - 10 - ] - }, - "fully_connected/biases/Adam_1": { - "filename": "fully_connected_biases_Adam_1", - "shape": [ - 10 - ] - }, "fully_connected/weights": { "filename": "fully_connected_weights", "shape": [ - 50, - 10 - ] - }, - "fully_connected/weights/Adam": { - "filename": "fully_connected_weights_Adam", - "shape": [ - 50, - 10 - ] - }, - "fully_connected/weights/Adam_1": { - "filename": "fully_connected_weights_Adam_1", - "shape": [ - 50, + 20, 10 ] }, "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias": { "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias", "shape": [ - 200 - ] - }, - "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias/Adam": { - "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias_Adam", - "shape": [ - 200 - ] - }, - "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias/Adam_1": { - "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_bias_Adam_1", - "shape": [ - 200 + 80 ] }, "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel": { "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel", "shape": [ - 60, - 200 - ] - }, - "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel/Adam": { - "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel_Adam", - "shape": [ - 60, - 200 - ] - }, - "rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel/Adam_1": { - "filename": "rnn_multi_rnn_cell_cell_0_basic_lstm_cell_kernel_Adam_1", - "shape": [ - 60, - 200 + 30, + 80 ] }, "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias": { "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias", "shape": [ - 200 - ] - }, - "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias/Adam": { - "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias_Adam", - "shape": [ - 200 - ] - }, - "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias/Adam_1": { - "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_bias_Adam_1", - "shape": [ - 200 + 80 ] }, "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel": { "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel", "shape": [ - 100, - 200 - ] - }, - "rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel/Adam": { - "filename": "rnn_multi_rnn_cell_cell_1_basic_lstm_cell_kernel_Adam", - "shape": [ - 100, - 200 - ] - }, - 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+31,10 @@ def main(unused_argv): tf.reset_default_graph() - x = tf.placeholder(dtype=tf.int32, shape=[1, 19]) - y = tf.placeholder(dtype=tf.int32, shape=[1, 19]) + x = tf.placeholder(dtype=tf.int32, shape=[1, data.shape[1] - 1]) + y = tf.placeholder(dtype=tf.int32, shape=[1, data.shape[1] - 1]) - NHIDDEN = 50 + NHIDDEN = 20 NLABELS = 10 lstm1 = tf.contrib.rnn.BasicLSTMCell(NHIDDEN) diff --git a/src/math/math_gpu_test.ts b/src/math/math_gpu_test.ts index 8b68af880b..546776af27 100644 --- a/src/math/math_gpu_test.ts +++ b/src/math/math_gpu_test.ts @@ -2188,3 +2188,62 @@ describe('NDArrayMathGPU debug mode', () => { expect(res.getValues()).toEqual(new Float32Array([2, NaN])); }); }); + +describe('LSTMCell', () => { + let math: NDArrayMathGPU; + beforeEach(() => { + math = new NDArrayMathGPU(); + math.startScope(); + }); + + afterEach(() => { + math.endScope(null!); + math.startScope(); + }); + + it('MultiRNNCell with 2 BasicLSTMCells', () => { + const lstmKernel1 = Array2D.new([3, 4], new Float32Array([ + 0.26242125034332275, -0.8787832260131836, 0.781475305557251, + 1.337337851524353, 0.6180247068405151, -0.2760246992111206, + -0.11299663782119751, -0.46332040429115295, -0.1765323281288147, + 0.6807947158813477, -0.8326982855796814, 0.6732975244522095])); + const lstmBias1 = Array1D.new(new Float32Array([ + 1.090713620185852, -0.8282332420349121, 0, 1.0889357328414917])); + const lstmKernel2 = Array2D.new([2, 4], new Float32Array([ + -1.893059492111206, -1.0185645818710327, -0.6270437240600586, + -2.1829540729522705, -0.4583775997161865, -0.5454602241516113, + -0.3114445209503174, 0.8450229167938232])); + const lstmBias2 = Array1D.new(new Float32Array([ + 0.9906240105628967, 0.6248329877853394, 0, 1.0224634408950806])); + + const forgetBias = Scalar.new(1.0); + const lstm1 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel1, + lstmBias1); + const lstm2 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel2, + lstmBias2); + + const c = [Array2D.zeros([1, lstmBias1.shape[0] / 4]), + Array2D.zeros([1, lstmBias2.shape[0] / 4])]; + const h = [Array2D.zeros([1, lstmBias1.shape[0] / 4]), + Array2D.zeros([1, lstmBias2.shape[0] / 4])]; + + const onehot = Array1D.zeros([2]); + onehot.set(1.0, 0); + + const output = math.multiRNNCell([lstm1, lstm2], onehot, c, h); + + test_util.expectArraysClose( + output[0][0].getValues(), new Float32Array([-0.7440074682235718]), + 1e-4); + test_util.expectArraysClose( + output[0][1].getValues(), new Float32Array([0.7460772395133972]), + 1e-4); + test_util.expectArraysClose( + output[1][0].getValues(), new Float32Array([-0.5802832245826721]), + 1e-4); + test_util.expectArraysClose( + output[1][1].getValues(), new Float32Array([0.5745711922645569]), + 1e-4); + }); +}); + From f3657bceda92863da6bdd2952c1431a20285d816 Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Mon, 14 Aug 2017 17:06:59 -0700 Subject: [PATCH 08/11] lint --- demos/lstm/lstm.ts | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/demos/lstm/lstm.ts b/demos/lstm/lstm.ts index 10eba9b65f..c408104fac 100644 --- a/demos/lstm/lstm.ts +++ b/demos/lstm/lstm.ts @@ -39,8 +39,10 @@ reader.getAllVariables().then(vars => { math.scope((keep, track) => { const forgetBias = track(Scalar.new(1.0)); - const lstm1 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel1, lstmBias1); - const lstm2 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel2, lstmBias2); + const lstm1 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel1, + lstmBias1); + const lstm2 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel2, + lstmBias2); let c = [track(Array2D.zeros([1, lstmBias1.shape[0] / 4])), track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; From fe7296c1ad4ecda83026e0fdb4313c50d736a130 Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Mon, 14 Aug 2017 17:12:44 -0700 Subject: [PATCH 09/11] lint --- demos/lstm/lstm.ts | 15 ++++++++------- src/math/math.ts | 30 +++++++++++++++--------------- 2 files changed, 23 insertions(+), 22 deletions(-) diff --git a/demos/lstm/lstm.ts b/demos/lstm/lstm.ts index c408104fac..9dddea6213 100644 --- a/demos/lstm/lstm.ts +++ b/demos/lstm/lstm.ts @@ -13,12 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -import {Array1D, Array2D, CheckpointLoader, NDArrayMathGPU, Scalar} from '../deeplearnjs'; +import {Array1D, Array2D, CheckpointLoader, NDArrayMathGPU, + Scalar} from '../deeplearnjs'; // manifest.json lives in the same directory. const reader = new CheckpointLoader('.'); reader.getAllVariables().then(vars => { - const primer_data = 3; + const primerData = 3; const expected = [1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8, 9, 7, 9, 3, 2, 3, 8, 4]; const math = new NDArrayMathGPU(); @@ -49,7 +50,7 @@ reader.getAllVariables().then(vars => { let h = [track(Array2D.zeros([1, lstmBias1.shape[0] / 4])), track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; - let input = primer_data; + let input = primerData; for (let i = 0; i < expected.length; i++) { const onehot = track(Array1D.zeros([10])); onehot.set(1.0, input); @@ -59,8 +60,8 @@ reader.getAllVariables().then(vars => { c = output[0]; h = output[1]; - const output_h = h[1]; - const weightedResult = math.matMul(output_h, fullyConnectedWeights); + const outputH = h[1]; + const weightedResult = math.matMul(outputH, fullyConnectedWeights); const weightedResult1D = math.reshape( weightedResult, [fullyConnectedBiases.shape[0]]) as Array1D; const logits = math.add( @@ -82,11 +83,11 @@ reader.getAllVariables().then(vars => { }); function checkArrays(expected: number[], results: number[]) { - if(expected.length != results.length) { + if(expected.length !== results.length) { return false; } for(let i = 0; i < expected.length; i++) { - if(expected[i] != results[i]) { + if(expected[i] !== results[i]) { return false; } } diff --git a/src/math/math.ts b/src/math/math.ts index b6b5603017..d0542177f4 100644 --- a/src/math/math.ts +++ b/src/math/math.ts @@ -23,7 +23,7 @@ export type ScopeResult = NDArray[]|NDArray|void; export interface LSTMCell { (data: Array1D, c: Array2D, h: Array2D): Array2D[]; -}; +} export abstract class NDArrayMath { @@ -1150,23 +1150,23 @@ export abstract class NDArrayMath { h: Array2D[]): Array2D[][] { const res = this.scope((keep, track) => { let input = data; - const new_states = [] + const newStates = []; for (let i = 0; i < lstmCells.length; i++) { const output = lstmCells[i](input, c[i], h[i]); - new_states.push(output[0]); - new_states.push(output[1]); + newStates.push(output[0]); + newStates.push(output[1]); input = this.reshape(output[1], [output[1].shape[1]]) as Array1D; } - return new_states; + return newStates; }); - const new_c:Array2D[] = []; - const new_h:Array2D[] = []; + const newC:Array2D[] = []; + const newH:Array2D[] = []; for (let i = 0; i < res.length; i += 2) { - new_c.push(res[i] as Array2D); - new_h.push(res[i + 1] as Array2D); + newC.push(res[i] as Array2D); + newH.push(res[i + 1] as Array2D); } - return [new_c, new_h]; + return [newC, newH]; } /** @@ -1196,7 +1196,7 @@ export abstract class NDArrayMath { // tf.nn.bias_add(weighted, lstmBias) // There is no broadcast add, but we can assume a batch size of 1, - // just reshape and do a normal add. + // so just reshape and do a normal add. const weighted1D = this.reshape(weighted, [lstmBias.shape[0]]) as Array1D; const res1D = this.add(weighted1D, lstmBias); // Convert back to 2D so we can do slice2D operations. @@ -1211,14 +1211,14 @@ export abstract class NDArrayMath { const o = this.slice2D(res, [0, res.shape[1] / 4 * 3], [res.shape[0], res.shape[1] / 4]); - const new_c = this.add( + const newC = this.add( this.elementWiseMul(c, this.sigmoid(this.scalarPlusArray(forgetBias, f))), this.elementWiseMul(this.sigmoid(i), this.tanh(j))) as Array2D; - const new_h = this.elementWiseMul( - this.tanh(new_c), this.sigmoid(o)) as Array2D; + const newH = this.elementWiseMul( + this.tanh(newC), this.sigmoid(o)) as Array2D; - return [new_c, new_h]; + return [newC, newH]; }); } From c881ee6514bebcec712f338147c640b47ab72607 Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Wed, 16 Aug 2017 10:50:11 -0700 Subject: [PATCH 10/11] fixes --- demos/lstm/README.md | 8 +++---- demos/lstm/index.html | 13 ++++++++++- demos/lstm/{lstm.ts => lstm_inference.ts} | 28 +++++------------------ 3 files changed, 22 insertions(+), 27 deletions(-) rename demos/lstm/{lstm.ts => lstm_inference.ts} (79%) diff --git a/demos/lstm/README.md b/demos/lstm/README.md index 9dd532eebf..0bba1fc6ed 100644 --- a/demos/lstm/README.md +++ b/demos/lstm/README.md @@ -1,10 +1,10 @@ -# LSTM Demo +# Learning digits of pi using an LSTM Demonstrates training a simple autoregressive LSTM network in Tensorflow and then porting that model to deeplearn.js. -This network uses two BasicLSTMCells combined with MultiRNNCell. The network is -trained the memorize the first few digits of pi. +This network uses two ``BasicLSTMCell``s combined with ``MultiRNNCell``. The +network is trained to memorize the first few digits of pi. First, train the LSTM network with Tensorflow: @@ -12,7 +12,7 @@ First, train the LSTM network with Tensorflow: python demos/lstm/train.py ``` -Next, export the weights to be used by deeplearn.js.: +Next, export the weights to be used by deeplearn.js: ``` python scripts/dump_checkpoint_vars.py --output_dir=demos/lstm/ --checkpoint_file=/tmp/simple_lstm-1000 --remove_variables_regex=".*Adam.*|.*beta.*" diff --git a/demos/lstm/index.html b/demos/lstm/index.html index 6da5354528..82bb347816 100644 --- a/demos/lstm/index.html +++ b/demos/lstm/index.html @@ -1,7 +1,17 @@ + LSTM Demo -

LSTM demo

@@ -10,5 +20,6 @@

LSTM demo

Results:
+ diff --git a/demos/lstm/lstm.ts b/demos/lstm/lstm_inference.ts similarity index 79% rename from demos/lstm/lstm.ts rename to demos/lstm/lstm_inference.ts index 9dddea6213..5a7c1f8dc5 100644 --- a/demos/lstm/lstm.ts +++ b/demos/lstm/lstm_inference.ts @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -import {Array1D, Array2D, CheckpointLoader, NDArrayMathGPU, - Scalar} from '../deeplearnjs'; +import {Array1D, Array2D, CheckpointLoader, NDArrayMathGPU, Scalar, + util} from '../deeplearnjs'; // manifest.json lives in the same directory. const reader = new CheckpointLoader('.'); @@ -36,7 +36,7 @@ reader.getAllVariables().then(vars => { const fullyConnectedBiases = vars['fully_connected/biases'] as Array1D; const fullyConnectedWeights = vars['fully_connected/weights'] as Array2D; - const results:number[] = []; + const results: number[] = []; math.scope((keep, track) => { const forgetBias = track(Scalar.new(1.0)); @@ -51,7 +51,7 @@ reader.getAllVariables().then(vars => { track(Array2D.zeros([1, lstmBias2.shape[0] / 4]))]; let input = primerData; - for (let i = 0; i < expected.length; i++) { + for (let i = 0; i < expected.length; i++) { const onehot = track(Array1D.zeros([10])); onehot.set(1.0, input); @@ -62,11 +62,7 @@ reader.getAllVariables().then(vars => { const outputH = h[1]; const weightedResult = math.matMul(outputH, fullyConnectedWeights); - const weightedResult1D = math.reshape( - weightedResult, [fullyConnectedBiases.shape[0]]) as Array1D; - const logits = math.add( - weightedResult1D, - fullyConnectedBiases); + const logits = math.add( weightedResult, fullyConnectedBiases); const result = math.argMax(logits).get(); results.push(result); @@ -75,21 +71,9 @@ reader.getAllVariables().then(vars => { }); document.getElementById('expected').innerHTML = '' + expected; document.getElementById('results').innerHTML = '' + results; - if(checkArrays(expected, results)) { + if(util.arraysEqual(expected, results)) { document.getElementById('success').innerHTML = 'Success!'; } else { document.getElementById('success').innerHTML = 'Failure.'; } }); - -function checkArrays(expected: number[], results: number[]) { - if(expected.length !== results.length) { - return false; - } - for(let i = 0; i < expected.length; i++) { - if(expected[i] !== results[i]) { - return false; - } - } - return true; -} From 06729312d7071c8888105ac00b117e147899ba76 Mon Sep 17 00:00:00 2001 From: Curtis Hawthorne Date: Wed, 16 Aug 2017 11:35:30 -0700 Subject: [PATCH 11/11] updates based on comments --- demos/lstm/lstm_inference.ts | 4 +-- src/math/math.ts | 56 +++++++++++++++++++----------------- src/math/math_gpu_test.ts | 43 +++++++++++++++++++++++++-- 3 files changed, 73 insertions(+), 30 deletions(-) diff --git a/demos/lstm/lstm_inference.ts b/demos/lstm/lstm_inference.ts index 5a7c1f8dc5..7e38e127d0 100644 --- a/demos/lstm/lstm_inference.ts +++ b/demos/lstm/lstm_inference.ts @@ -52,8 +52,8 @@ reader.getAllVariables().then(vars => { let input = primerData; for (let i = 0; i < expected.length; i++) { - const onehot = track(Array1D.zeros([10])); - onehot.set(1.0, input); + const onehot = track(Array2D.zeros([1, 10])); + onehot.set(1.0, 0, input); const output = math.multiRNNCell([lstm1, lstm2], onehot, c, h); diff --git a/src/math/math.ts b/src/math/math.ts index d0542177f4..13a02935e7 100644 --- a/src/math/math.ts +++ b/src/math/math.ts @@ -22,7 +22,7 @@ import {Array1D, Array2D, Array3D, Array4D, NDArray, Scalar} from './ndarray'; export type ScopeResult = NDArray[]|NDArray|void; export interface LSTMCell { - (data: Array1D, c: Array2D, h: Array2D): Array2D[]; + (data: Array2D, c: Array2D, h: Array2D): [Array2D, Array2D]; } @@ -1140,28 +1140,34 @@ export abstract class NDArrayMath { /** * Computes the next states and outputs of a stack of LSTMCells. * Each cell output is used as input to the next cell. + * This is only the forward mode. * Derived from tf.contrib.rn.MultiRNNCell. * @param lstmCells Array of LSTMCell functions. * @param data The input to the cell. * @param c Array of previous cell states. * @param h Array of previous cell outputs. + * @return Tuple [nextCellStates, cellOutputs] */ - multiRNNCell(lstmCells: LSTMCell[], data: Array1D, c: Array2D[], - h: Array2D[]): Array2D[][] { - const res = this.scope((keep, track) => { + multiRNNCell(lstmCells: LSTMCell[], data: Array2D, c: Array2D[], + h: Array2D[]): [Array2D[], Array2D[]] { + util.assert( + data.shape[0] === 1, + `Error in multiRNNCell: first dimension of data is ${data.shape[0]}, ` + + `but batch sizes > 1 are not yet supported.`); + const res = this.scope(() => { let input = data; const newStates = []; for (let i = 0; i < lstmCells.length; i++) { const output = lstmCells[i](input, c[i], h[i]); newStates.push(output[0]); newStates.push(output[1]); - input = this.reshape(output[1], [output[1].shape[1]]) as Array1D; + input = output[1]; } return newStates; }); - const newC:Array2D[] = []; - const newH:Array2D[] = []; + const newC: Array2D[] = []; + const newH: Array2D[] = []; for (let i = 0; i < res.length; i += 2) { newC.push(res[i] as Array2D); newH.push(res[i + 1] as Array2D); @@ -1171,6 +1177,7 @@ export abstract class NDArrayMath { /** * Computes the next state and output of a BasicLSTMCell. + * This is only the forward mode. * Derived from tf.contrib.rnn.BasicLSTMCell. * @param forgetBias Forget bias for the cell. * @param lstmKernel The weights for the cell. @@ -1178,29 +1185,25 @@ export abstract class NDArrayMath { * @param data The input to the cell. * @param c Previous cell state. * @param h Previous cell output. + * @return Tuple [nextCellState, cellOutput] */ basicLSTMCell(forgetBias: Scalar, lstmKernel: Array2D, lstmBias: Array1D, - data: Array1D, c: Array2D, h: Array2D): Array2D[] { - return this.scope((keep, track) => { + data: Array2D, c: Array2D, h: Array2D): [Array2D, Array2D] { + const res = this.scope(() => { + util.assert( + data.shape[0] === 1, + `Error in multiRNNCell: first dimension of data is ` + + `${data.shape[0]}, but batch sizes > 1 are not yet supported.`); // concat(inputs, h, 1) // There is no concat1d, so reshape inputs and h to 3d, concat, then - // reshape back to 1d. - const data3D = this.reshape( - data, [1, 1, data.shape[0]]) as Array3D; - const h3D = this.reshape(h, [1, 1, h.shape[1]]) as Array3D; + // reshape back to 2d. + const data3D = data.as3D(1, 1, data.shape[1]); + const h3D = h.as3D(1, 1, h.shape[1]); const combined3D = this.concat3D(data3D, h3D, 2); - const combined2D = this.reshape( - combined3D, [1, data.shape[0] + h.shape[1]]) as Array2D; + const combined2D = combined3D.as2D(1, data.shape[1] + h.shape[1]); const weighted = this.matMul(combined2D, lstmKernel); - - // tf.nn.bias_add(weighted, lstmBias) - // There is no broadcast add, but we can assume a batch size of 1, - // so just reshape and do a normal add. - const weighted1D = this.reshape(weighted, [lstmBias.shape[0]]) as Array1D; - const res1D = this.add(weighted1D, lstmBias); - // Convert back to 2D so we can do slice2D operations. - const res = this.reshape(res1D, [1, res1D.shape[0]]) as Array2D; + const res = this.add(weighted, lstmBias) as Array2D; // i = input_gate, j = new_input, f = forget_gate, o = output_gate const i = this.slice2D(res, [0, 0], [res.shape[0], res.shape[1] / 4]); @@ -1212,14 +1215,15 @@ export abstract class NDArrayMath { [res.shape[0], res.shape[1] / 4]); const newC = this.add( - this.elementWiseMul(c, + this.multiplyStrict(c, this.sigmoid(this.scalarPlusArray(forgetBias, f))), - this.elementWiseMul(this.sigmoid(i), this.tanh(j))) as Array2D; - const newH = this.elementWiseMul( + this.multiplyStrict(this.sigmoid(i), this.tanh(j))) as Array2D; + const newH = this.multiplyStrict( this.tanh(newC), this.sigmoid(o)) as Array2D; return [newC, newH]; }); + return [res[0], res[1]]; } } diff --git a/src/math/math_gpu_test.ts b/src/math/math_gpu_test.ts index 546776af27..79abe19349 100644 --- a/src/math/math_gpu_test.ts +++ b/src/math/math_gpu_test.ts @@ -2201,6 +2201,45 @@ describe('LSTMCell', () => { math.startScope(); }); + it('Batch size must be 1 for MultiRNNCell', () => { + const lstmKernel1 = Array2D.zeros([3, 4]); + const lstmBias1 = Array1D.zeros([4]); + const lstmKernel2 = Array2D.zeros([2, 4]); + const lstmBias2 = Array1D.zeros([4]); + + const forgetBias = Scalar.new(1.0); + const lstm1 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel1, + lstmBias1); + const lstm2 = math.basicLSTMCell.bind(math, forgetBias, lstmKernel2, + lstmBias2); + + const c = [Array2D.zeros([1, lstmBias1.shape[0] / 4]), + Array2D.zeros([1, lstmBias2.shape[0] / 4])]; + const h = [Array2D.zeros([1, lstmBias1.shape[0] / 4]), + Array2D.zeros([1, lstmBias2.shape[0] / 4])]; + + const onehot = Array2D.zeros([2, 2]); + onehot.set(1.0, 1, 0); + const output = () => math.multiRNNCell([lstm1, lstm2], onehot, c, h); + expect(output).toThrowError(); + }); + + it('Batch size must be 1 for basicLSTMCell', () => { + const lstmKernel = Array2D.zeros([3, 4]); + const lstmBias = Array1D.zeros([4]); + + const forgetBias = Scalar.new(1.0); + + const c = Array2D.zeros([1, lstmBias.shape[0] / 4]); + const h = Array2D.zeros([1, lstmBias.shape[0] / 4]); + + const onehot = Array2D.zeros([2, 2]); + onehot.set(1.0, 1, 0); + const output = () => math.basicLSTMCell(forgetBias, lstmKernel, + lstmBias, onehot, c, h); + expect(output).toThrowError(); + }); + it('MultiRNNCell with 2 BasicLSTMCells', () => { const lstmKernel1 = Array2D.new([3, 4], new Float32Array([ 0.26242125034332275, -0.8787832260131836, 0.781475305557251, @@ -2227,8 +2266,8 @@ describe('LSTMCell', () => { const h = [Array2D.zeros([1, lstmBias1.shape[0] / 4]), Array2D.zeros([1, lstmBias2.shape[0] / 4])]; - const onehot = Array1D.zeros([2]); - onehot.set(1.0, 0); + const onehot = Array2D.zeros([1, 2]); + onehot.set(1.0, 0, 0); const output = math.multiRNNCell([lstm1, lstm2], onehot, c, h);

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