From 3bcf405c9a4d138f666a467d1026d9dae848f748 Mon Sep 17 00:00:00 2001 From: Gabriel Rasskin <43894452+grasskin@users.noreply.github.com> Date: Fri, 10 Nov 2023 19:58:05 +0000 Subject: [PATCH] Convert conv_lstm --- examples/vision/conv_lstm.py | 9 +- .../vision/img/conv_lstm/conv_lstm_13_1.png | Bin 0 -> 104795 bytes .../vision/img/conv_lstm/conv_lstm_7_1.png | Bin 54373 -> 93438 bytes examples/vision/ipynb/conv_lstm.ipynb | 15 +- examples/vision/md/conv_lstm.md | 147 ++++++++++++++++-- 5 files changed, 147 insertions(+), 24 deletions(-) create mode 100644 examples/vision/img/conv_lstm/conv_lstm_13_1.png diff --git a/examples/vision/conv_lstm.py b/examples/vision/conv_lstm.py index 88a1d241ef..b17725dea9 100644 --- a/examples/vision/conv_lstm.py +++ b/examples/vision/conv_lstm.py @@ -2,7 +2,7 @@ Title: Next-Frame Video Prediction with Convolutional LSTMs Author: [Amogh Joshi](https://github.com/amogh7joshi) Date created: 2021/06/02 -Last modified: 2021/06/05 +Last modified: 2023/11/10 Description: How to build and train a convolutional LSTM model for next-frame video prediction. Accelerator: GPU """ @@ -25,9 +25,8 @@ import numpy as np import matplotlib.pyplot as plt -import tensorflow as tf -from tensorflow import keras -from tensorflow.keras import layers +import keras +from keras import layers import io import imageio @@ -279,7 +278,7 @@ def create_shifted_frames(data): # Construct a GIF from the frames. with io.BytesIO() as gif: - imageio.mimsave(gif, current_frames, "GIF", fps=5) + imageio.mimsave(gif, current_frames, "GIF", duration=200) predicted_videos.append(gif.getvalue()) # Display the videos. diff --git a/examples/vision/img/conv_lstm/conv_lstm_13_1.png b/examples/vision/img/conv_lstm/conv_lstm_13_1.png new file mode 100644 index 0000000000000000000000000000000000000000..c962988d8d7693c6c915c46455c3d061bb9b2f53 GIT binary patch literal 104795 zcmd43c|4WxzBjH>Ga(H`goG#+LX=2`h%zKolFaiw*0*FziX^QNlEfmh3<-&qB&p0Z zOGu`Wd3xTr_Bm(o^ZV!b*YkS*IQ#5ftnPJPpX>8^PuCrEPEB$1hFu%z=;$^posrk1 zqg%^GN4G+2-CF!fP^79l{@*cY1zqR!c4p3QmmROtoxSXAZ*AvnZDF#<^{S(jg`MqT zVF}?wLVL`eo$Z|@MMP}=`v-*W9IuIdPh*zGhpe|hbJ2;8j^Q%#e=AT;^g^eRtv`)0@o9EAMhlkEVXUV|JvlL)6W7q;D#~uuu0&MUJV3*|X@r z1<(F9Z_W=Nqjj}96;+S>aKt3=Z;)HZb&K(T{-YtTXJa|l>X;)m;alI))}V%^+&04;s5rp6Mw0@E&Sgf2!B-e@*dCs`Xg=s z|L-H&Hu98375(9LmTbGa4Uet-L`u z)#}8sPd!$@;AZxNppyD%`6_-?8%o#$Jb8r#WN)9mYmH2 zr6;Yd1dD%fEPj{o)F(W*uxiz+x1*tUW&O@rshJ+5#j7Tnjk`D}A`hoet#})*?(^%s zv$J!WyA9T3!1+jnjxrUe>p7QFLviMI!@!~|P`vX!$S=$k z&Iz&3e2*$ByVO3$S~OE-H}=J?$Z2ScH}k!`f=)NzKDTMRrJ>@{VYgyZnD>vpPmX&% z4B+q#JbdlT?^4c1`=KqK%#9xpO(pxyCeIIElkGk};=v&0Hf-wa8(Zx=nKjr}5Opoq z!;I>ormpT(zT2&H)P4F-vg}zgQqSSInN7)RF|^-ZrH`t5U8EzTp0++Mnx={fTzCBO z@xI_9{!jP!DzcoIp=~dAZyJe-i_;mKd7Ho=JVrdG zmTl)!zpWmVlL&vL@+acoS20{#=veYubB@F3&$l|6+Owx6h+p)#vFoY|2>tl_4M*qZa-6v0l>NR&SnR?O*-<8n%TK+DV2PRv8CGLx2h=@XdO#)e(DnJANrlg;u(AbHB+1`GQDRijm$cVd~m$yePxTM zLyAAv*47Hf-lUqu$H#XCe-UR2GR|sBALLX_3$9{uGRul?u`6B(Z|E4k>qfUd$2*9U z8BH5b%FfMQbx%3TaAJf}$2RLjYq{*A!|d#=d&gY6o0jap^suU3ZWO&xXGtF|*}ds; zRjh7H-G1{7lOr9~QthK#eoef|4%DS59q2VoyC5)HW#=0qHS#vX{K)J}-+(+%(t+Xc z{YSd&-Kk%Ea$7=c`X3yPJerMv7svFu9(gPT4=!k^v$R;3;XAR-FCfpAjjPwTcwth- z_28dBe_q+N$Qg{4TZxle6aW zT1#14r1$$x|ES7LW->EOLm=`Xsfsu-I2(R6;$ZCFy<12~=(o7vxOl5aUkv_kGTfDe zsHHnqX5HPKOA>qc?v+L{JW}QR<9S>~MP_#{|=B<6=Lm<58$BPl^X7x9_@B7Y}Z7k1(t&bM_e?3P{VS5K;I6^O|5Iw=!Z zKI9rv-8VYp{dTHwq(^<}kGg|oOvBLevNcR@+gW>RcRCf%6%<#VVmK}@MoLOdyeT^q zA=^bfRr<{FvW+~w!XrcrO}P+kG@E3k7uiwVgr0BTVBm({?0u7HCb>luWR?&GqWwLL zikkVTDC20*e(LQB+7_Fwvs5MFg0?O1z9lMV5T7S@>GS^J_wV1YY{{{yF(gGtUv_Y~ zf5|q#Cw!!%*iROQNjJF5HsUt*-n1q~L3nF@8^1Ultw*OKd>pV1Ox6=||P zEGG7xI96!*4<;YaO6Ayfq(7*3e7_W=RqCe!k@M(Pk0(M75f#BwNDl4NO3;fUE{LPA@@6R1bT?B`yj(- z$m)~qe5ow|asxWyQDtSActIdi(3f*`jAz);ez5V-(BY#@LHuz`^L>#4oW8om=Ov)d zNBoxNbmnDfN!Zl4ZiNE|!Bms`*McyMY?J)`*Yse|4Ms;8H?55%epP3lgBGdOTG;vX zTOyib3FmxCaD(6Po1FaDY0i=v(>`S?E!KyUXXlESyw;p6`t2`bo{=&-O#p_%A9KWH zytLwhNv3<<2&LI}I)*WBuC?#y(2!3>hF|9@&ODwL^Gx0BCBxLZGfA2fF={yA60U=d zyv5xMBYv`c5lVA&H&)S42l!3X`}XMgUZqa$maxBGJYQM-Opk1GWYNM$M`RaA!)E6DWJRRBwQrzxcPuSV?Pm?y zv}x1t9udEbdZ*J?VmuOH$l+J*{vy<$epJUkVw2VoXYeg;glJ}G?CeCAW@MMHE&ssl z7$beQAgPaLS?~N~ZdXf>Z=&DAPKQp;@sy}_Y|sysare>Uu)Gr{!jLNydjXcA~BkhIk z`0BC?HL^+h`OyUu+pkdHr?*E_{1(x`e!Ttebw@j~v>!xr0uPzLYL zRm}sIss`SM0;zQ`eX4tkiehc2dCC?S=0pk#%cDb9 zH%@ehj}+(mv0g9lUhy<3sVBT)U_!F8vU2sx6}JNdG+6v&V`D2hTBs`oZ8?~nqGp>L ziav}iw2zFY0^ZGku$%wz@@=^pQM7Gt9h={g+W+viPv+!i`pmgLQQHShR_xK%VU-pB zsNmI}u^@Hte>xV2I*NWgD*JUW&&y(|OLm@K2D>ebh4 z`hSq^d<2aa$D%0o<_<&H;0dC?n`H8nA8FsmevNokdmY>rIzKMElr`Dh(mpBX>FGIL zjeQ%(fF62N7af^SFXMXIHtyl(MM2nwZ@I`-K zTI^Wz`ZY1j+u=95bWdmOO#=Tr|2_4<K&mLS=83VFquzt9ZSB$#Y?`6 zvl&aXZTKCE#G4It0b=B(~62SB@DjrmS&BXtf&LN#c>TqlN18L z6$p>)O$W4=y58Gs<~v&xCn_!dd468{`0?XMjvV<~Tl?p=)*NlNW!<6Nw{JfayRMnS zqJDg$miMsWE;hCmk4U6~AJ_VNdwOtenB}mY__kwxfS+%ShHt8IU}e zo0_^&LF4DopZ7vO@#Sh<8E(23*Ry-Xoj$kKabuYDSm1^7He)f%Rs%KEzV$<+G;%1b z69$*ifdk|nV@b0>kyXjE3l?`dWxou!73d7@C=+ZkcbL+|85?zfa*jW{cjSODd>kJiABk|F0nX$1PeA+*kG26FQrE& zhD^l(KDW0v<-ABik7imZR%jXB6BBit9{~*4UMIen_rRr&H-oN*nK@#5t=mErM590R zW-OuCuLw>!W3;>$Or*Pw#*BCe;4y|M5d;DS{yZyd{7sR%j?Q*J%#x4$ zWJcG6ym|mp->vL10p}uiXTCXOVHf9IW4Ym^n>T1BZ=~`eDXER`WS7Qet;l;8+luGh znjiiQ3R+K4D2@}A&I<%hrn9lJnI98b8gn@=?%*P(_xY;m&i3E8#b0G-m)R{%+KsoN z<%f5>=ZD1WvauZ)21h?|;DGdW=gq&gv?S$x@-+BLV0m{J3hjr>aS?|Je9REIQK8pV z`t&i%ftnnf7I!?YuR*oSTSPky=lw@f`2<;FhWb@T;S&j3m`b@bIp|B{pxe67)Fri;YK}m1l;6EK{G_#E$nh${mqlg+a72PSbG*f+^pJlpipNzwQJrbSF*!?i@ z#il|p@knL=4DTL6cBl>n{3|dmx~}UbuJU=Id}*3Qcw7c6bF3(ZhAT7w|RW zCOiD5-29?4{1ygULI1%fIk`wx%VcY?G4m#a&(Bj06K~U_Hb>tgX1jr7KD*J(C!J|x z^0XIv&47be0w9eJEE;kyO_%o%4fRwn%~#J51h4b`({SQAN&kMgDr0&)Zszl~M~@zT zU0e)Xnhu-qmR;Oy&y`oZvAMCak$0p0ua1$o<7!HBX<0ZhLGp5y&kcv!Wq#jaIKxC+ z`|2C*i*4?B7lYp;Y}P*SZnlo6=lIe2%79}HeMhEz!zL#u2Yd}_zpS$#F^dO;vbq^& zL=lxKJ^M3p8A5}$3=iAN07#E#hWV(z{AS09J?(ri9Z1X)@4+ja-Nd7(To7n=ZLwJ< zR1N6{4GV9}>Y4%%(H}f=qzr&}=BJ{pKtnFu*VA_jmNgapjCa>Yqp#sxa@#!xK?D|! 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zNCQh!23vw3lynV(o|`xPkWlGBW?UIK_r}{@WJht$!;oaqM9$#KgH=@b$DQ1#HN!p{?0j%|ViUDoHmWqSu=J$DKXvN< zxw{@GaG@ROYBLRQK$s4a>XHJ2<%ts;)siZ6J4|=KdCDI=zrpf@gos3g3RajSTDYS`OBhB;p9Ug3DOd@p-v`{Uk9=NzVka?Mdm zH?SO7!ijBI{e#|rTyExIDEuO}Dw{`GlPtXayPv^n3`-bwz-URDr4sCEi&6blB#CES z|0z!SRaZS|Ww!i*Q`Cq7gdZi\n", "**Date created:** 2021/06/02
\n", - "**Last modified:** 2021/06/05
\n", + "**Last modified:** 2023/11/10
\n", "**Description:** How to build and train a convolutional LSTM model for next-frame video prediction." ] }, @@ -50,9 +50,8 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "import tensorflow as tf\n", - "from tensorflow import keras\n", - "from tensorflow.keras import layers\n", + "import keras\n", + "from keras import layers\n", "\n", "import io\n", "import imageio\n", @@ -116,6 +115,7 @@ "train_dataset = train_dataset / 255\n", "val_dataset = val_dataset / 255\n", "\n", + "\n", "# We'll define a helper function to shift the frames, where\n", "# `x` is frames 0 to n - 1, and `y` is frames 1 to n.\n", "def create_shifted_frames(data):\n", @@ -226,7 +226,8 @@ "# Next, we will build the complete model and compile it.\n", "model = keras.models.Model(inp, x)\n", "model.compile(\n", - " loss=keras.losses.binary_crossentropy, optimizer=keras.optimizers.Adam(),\n", + " loss=keras.losses.binary_crossentropy,\n", + " optimizer=keras.optimizers.Adam(),\n", ")" ] }, @@ -342,7 +343,7 @@ "and construct some GIFs with them to see the model's\n", "predicted videos.\n", "\n", - "You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/conv-lstm) ", + "You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/conv-lstm)\n", "and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/conv-lstm)." ] }, @@ -386,7 +387,7 @@ "\n", " # Construct a GIF from the frames.\n", " with io.BytesIO() as gif:\n", - " imageio.mimsave(gif, current_frames, \"GIF\", fps=5)\n", + " imageio.mimsave(gif, current_frames, \"GIF\", duration=200)\n", " predicted_videos.append(gif.getvalue())\n", "\n", "# Display the videos.\n", diff --git a/examples/vision/md/conv_lstm.md b/examples/vision/md/conv_lstm.md index 6fb6a0a99d..b5a3dc5be9 100644 --- a/examples/vision/md/conv_lstm.md +++ b/examples/vision/md/conv_lstm.md @@ -2,7 +2,7 @@ **Author:** [Amogh Joshi](https://github.com/amogh7joshi)
**Date created:** 2021/06/02
-**Last modified:** 2021/06/05
+**Last modified:** 2023/11/10
**Description:** How to build and train a convolutional LSTM model for next-frame video prediction. @@ -28,9 +28,8 @@ of predicting what video frames come next given a series of past frames. import numpy as np import matplotlib.pyplot as plt -import tensorflow as tf -from tensorflow import keras -from tensorflow.keras import layers +import keras +from keras import layers import io import imageio @@ -82,6 +81,7 @@ val_dataset = dataset[val_index] train_dataset = train_dataset / 255 val_dataset = val_dataset / 255 + # We'll define a helper function to shift the frames, where # `x` is frames 0 to n - 1, and `y` is frames 1 to n. def create_shifted_frames(data): @@ -101,6 +101,8 @@ print("Validation Dataset Shapes: " + str(x_val.shape) + ", " + str(y_val.shape)
``` +Downloading data from http://www.cs.toronto.edu/~nitish/unsupervised_video/mnist_test_seq.npy + 819200096/819200096 ━━━━━━━━━━━━━━━━━━━━ 116s 0us/step Training Dataset Shapes: (900, 19, 64, 64, 1), (900, 19, 64, 64, 1) Validation Dataset Shapes: (100, 19, 64, 64, 1), (100, 19, 64, 64, 1) @@ -132,7 +134,7 @@ plt.show()
``` -Displaying frames for example 130. +Displaying frames for example 95. ```
@@ -186,7 +188,8 @@ x = layers.Conv3D( # Next, we will build the complete model and compile it. model = keras.models.Model(inp, x) model.compile( - loss=keras.losses.binary_crossentropy, optimizer=keras.optimizers.Adam(), + loss=keras.losses.binary_crossentropy, + optimizer=keras.optimizers.Adam(), ) ``` @@ -216,6 +219,53 @@ model.fit( ) ``` +
+``` +Epoch 1/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 50s 226ms/step - loss: 0.1510 - val_loss: 0.2966 - learning_rate: 0.0010 +Epoch 2/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0287 - val_loss: 0.1766 - learning_rate: 0.0010 +Epoch 3/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0269 - val_loss: 0.0661 - learning_rate: 0.0010 +Epoch 4/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0264 - val_loss: 0.0279 - learning_rate: 0.0010 +Epoch 5/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0258 - val_loss: 0.0254 - learning_rate: 0.0010 +Epoch 6/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0256 - val_loss: 0.0253 - learning_rate: 0.0010 +Epoch 7/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0251 - val_loss: 0.0248 - learning_rate: 0.0010 +Epoch 8/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0251 - val_loss: 0.0251 - learning_rate: 0.0010 +Epoch 9/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0247 - val_loss: 0.0243 - learning_rate: 0.0010 +Epoch 10/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0246 - val_loss: 0.0246 - learning_rate: 0.0010 +Epoch 11/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0245 - val_loss: 0.0247 - learning_rate: 0.0010 +Epoch 12/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0241 - val_loss: 0.0243 - learning_rate: 0.0010 +Epoch 13/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0244 - val_loss: 0.0245 - learning_rate: 0.0010 +Epoch 14/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0241 - val_loss: 0.0241 - learning_rate: 0.0010 +Epoch 15/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0243 - val_loss: 0.0241 - learning_rate: 0.0010 +Epoch 16/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0242 - val_loss: 0.0242 - learning_rate: 0.0010 +Epoch 17/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0240 - val_loss: 0.0240 - learning_rate: 0.0010 +Epoch 18/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0240 - val_loss: 0.0243 - learning_rate: 0.0010 +Epoch 19/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0240 - val_loss: 0.0244 - learning_rate: 0.0010 +Epoch 20/20 + 180/180 ━━━━━━━━━━━━━━━━━━━━ 40s 219ms/step - loss: 0.0237 - val_loss: 0.0238 - learning_rate: 1.0000e-04 + + + +``` +
--- ## Frame Prediction Visualizations @@ -266,9 +316,23 @@ for idx, ax in enumerate(axes[1]): plt.show() ``` +
+``` + 1/1 ━━━━━━━━━━━━━━━━━━━━ 2s 2s/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 800ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 805ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 790ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 821ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 824ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 928ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 813ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 810ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 814ms/step +``` +
-![png](/img/examples/vision/conv_lstm/conv_lstm_13_0.png) +![png](/img/examples/vision/conv_lstm/conv_lstm_13_1.png) @@ -279,7 +343,8 @@ Finally, we'll pick a few examples from the validation set and construct some GIFs with them to see the model's predicted videos. -You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/conv-lstm) and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/conv-lstm). +You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/conv-lstm) +and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/conv-lstm). ```python @@ -315,7 +380,7 @@ for example in examples: # Construct a GIF from the frames. with io.BytesIO() as gif: - imageio.mimsave(gif, current_frames, "GIF", fps=5) + imageio.mimsave(gif, current_frames, "GIF", duration=200) predicted_videos.append(gif.getvalue()) # Display the videos. @@ -333,9 +398,67 @@ for i in range(0, len(predicted_videos), 2):
``` + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 790ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step + 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step Truth Prediction -``` -
+HBox(children=(Image(value=b'GIF89a@\x00@\x00\x87\x00\x00\xff\xff\xff\xfe\xfe\xfe\xfd\xfd\xfd\xfc\xfc\xfc\xf8\… + +HBox(children=(Image(value=b'GIF89a@\x00@\x00\x86\x00\x00\xff\xff\xff\xfd\xfd\xfd\xfc\xfc\xfc\xfb\xfb\xfb\xf4\… -![Imgur](https://i.imgur.com/UYMTsw7.gif) \ No newline at end of file +HBox(children=(Image(value=b'GIF89a@\x00@\x00\x86\x00\x00\xff\xff\xff\xfe\xfe\xfe\xfd\xfd\xfd\xfc\xfc\xfc\xfb\… + +HBox(children=(Image(value=b'GIF89a@\x00@\x00\x86\x00\x00\xff\xff\xff\xfe\xfe\xfe\xfd\xfd\xfd\xfc\xfc\xfc\xfb\… + +HBox(children=(Image(value=b'GIF89a@\x00@\x00\x86\x00\x00\xff\xff\xff\xfd\xfd\xfd\xfc\xfc\xfc\xf9\xf9\xf9\xf7\… + +``` +
\ No newline at end of file