From 3d28583570648734622c43bd2d1532db686fbe25 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Franc=CC=A7ois=20Chollet?= Date: Sun, 30 Aug 2020 22:22:11 -0700 Subject: [PATCH] Update generated files --- examples/keras_recipes/md/tfrecord.md | 2 +- .../ipynb/pretrained_word_embeddings.ipynb | 2 +- examples/nlp/md/pretrained_word_embeddings.md | 50 +++--- .../md/text_classification_from_scratch.md | 20 +-- examples/nlp/pretrained_word_embeddings.py | 2 +- .../vision/md/super_resolution_sub_pixel.md | 2 +- .../functional_api/functional_api_22_0.png | Bin 20290 -> 27270 bytes .../functional_api/functional_api_40_0.png | Bin 48623 -> 56155 bytes .../functional_api/functional_api_51_0.png | Bin 141166 -> 158837 bytes .../training_with_built_in_methods_64_0.png | Bin 54985 -> 53186 bytes guides/md/distributed_training.md | 31 ++-- guides/md/functional_api.md | 22 +-- guides/md/intro_to_keras_for_engineers.md | 30 ++-- guides/md/intro_to_keras_for_researchers.md | 128 +++++++-------- guides/md/preprocessing_layers.md | 6 +- guides/md/serialization_and_saving.md | 28 ++-- guides/md/training_with_built_in_methods.md | 149 ++++++++++-------- guides/md/working_with_rnns.md | 16 +- .../writing_a_training_loop_from_scratch.md | 88 +++++------ scripts/autogen.py | 4 +- .../intro_to_keras_for_engineers.md | 30 ++-- .../intro_to_keras_for_researchers.md | 128 +++++++-------- 22 files changed, 375 insertions(+), 363 deletions(-) diff --git a/examples/keras_recipes/md/tfrecord.md b/examples/keras_recipes/md/tfrecord.md index 039a61afac..81a05fc639 100644 --- a/examples/keras_recipes/md/tfrecord.md +++ b/examples/keras_recipes/md/tfrecord.md @@ -5,7 +5,7 @@ **Last modified:** 2020/08/07
- [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/keras_recipesipynb/tfrecord.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/keras_recipestfrecord.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/keras_recipes/ipynb/tfrecord.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/keras_recipes/tfrecord.py) **Description:** Loading TFRecords for computer vision models. diff --git a/examples/nlp/ipynb/pretrained_word_embeddings.ipynb b/examples/nlp/ipynb/pretrained_word_embeddings.ipynb index 676582ea03..2280206e50 100644 --- a/examples/nlp/ipynb/pretrained_word_embeddings.ipynb +++ b/examples/nlp/ipynb/pretrained_word_embeddings.ipynb @@ -308,7 +308,7 @@ "outputs": [], "source": [ "voc = vectorizer.get_vocabulary()\n", - "word_index = dict(zip(voc, range(2, len(voc))))" + "word_index = dict(zip(voc, range(len(voc))))" ] }, { diff --git a/examples/nlp/md/pretrained_word_embeddings.md b/examples/nlp/md/pretrained_word_embeddings.md index 7bd9c3cddf..f888a75fc0 100644 --- a/examples/nlp/md/pretrained_word_embeddings.md +++ b/examples/nlp/md/pretrained_word_embeddings.md @@ -6,7 +6,7 @@ **Description:** Text classification on the Newsgroup20 dataset using pre-trained GloVe word embeddings. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlpipynb/pretrained_word_embeddings.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlppretrained_word_embeddings.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/pretrained_word_embeddings.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlp/pretrained_word_embeddings.py) @@ -257,7 +257,7 @@ Here's a dict mapping words to their indices: ```python voc = vectorizer.get_vocabulary() -word_index = dict(zip(voc, range(2, len(voc)))) +word_index = dict(zip(voc, range(len(voc)))) ``` As you can see, we obtain the same encoding as above for our test sentence: @@ -273,7 +273,7 @@ test = ["the", "cat", "sat", "on", "the", "mat"]
``` -[4, 3699, 1688, 17, 4, 5945] +[2, 3697, 1686, 15, 2, 5943] ```
@@ -344,7 +344,7 @@ print("Converted %d words (%d misses)" % (hits, misses))
``` -Converted 17997 words (2001 misses) +Converted 17999 words (2001 misses) ```
@@ -453,45 +453,47 @@ model.fit(x_train, y_train, batch_size=128, epochs=20, validation_data=(x_val, y
``` Epoch 1/20 -125/125 [==============================] - 8s 59ms/step - loss: 3.0124 - acc: 0.0669 - val_loss: 2.9522 - val_acc: 0.0725 +125/125 [==============================] - 8s 57ms/step - loss: 2.8766 - acc: 0.0945 - val_loss: 2.0770 - val_acc: 0.2956 Epoch 2/20 -125/125 [==============================] - 7s 58ms/step - loss: 2.9439 - acc: 0.0888 - val_loss: 2.9447 - val_acc: 0.0818 +125/125 [==============================] - 7s 58ms/step - loss: 2.0792 - acc: 0.2887 - val_loss: 1.6626 - val_acc: 0.4076 Epoch 3/20 -125/125 [==============================] - 8s 60ms/step - loss: 2.8670 - acc: 0.1072 - val_loss: 2.8869 - val_acc: 0.1015 +125/125 [==============================] - 7s 60ms/step - loss: 1.5632 - acc: 0.4527 - val_loss: 1.3000 - val_acc: 0.5609 Epoch 4/20 -125/125 [==============================] - 8s 61ms/step - loss: 2.7579 - acc: 0.1387 - val_loss: 2.7014 - val_acc: 0.1370 +125/125 [==============================] - 8s 60ms/step - loss: 1.2945 - acc: 0.5612 - val_loss: 1.2282 - val_acc: 0.5944 Epoch 5/20 -125/125 [==============================] - 8s 61ms/step - loss: 2.6353 - acc: 0.1703 - val_loss: 2.5607 - val_acc: 0.1870 +125/125 [==============================] - 8s 61ms/step - loss: 1.1137 - acc: 0.6209 - val_loss: 1.0695 - val_acc: 0.6409 Epoch 6/20 -125/125 [==============================] - 8s 63ms/step - loss: 2.4557 - acc: 0.2215 - val_loss: 2.4721 - val_acc: 0.2081 +125/125 [==============================] - 8s 61ms/step - loss: 0.9556 - acc: 0.6718 - val_loss: 1.1743 - val_acc: 0.6124 Epoch 7/20 -125/125 [==============================] - 8s 64ms/step - loss: 2.3065 - acc: 0.2597 - val_loss: 2.3387 - val_acc: 0.2478 +125/125 [==============================] - 8s 61ms/step - loss: 0.8235 - acc: 0.7172 - val_loss: 1.0126 - val_acc: 0.6602 Epoch 8/20 -125/125 [==============================] - 8s 65ms/step - loss: 2.1402 - acc: 0.3081 - val_loss: 2.5133 - val_acc: 0.2226 +125/125 [==============================] - 8s 65ms/step - loss: 0.7268 - acc: 0.7475 - val_loss: 1.0608 - val_acc: 0.6632 Epoch 9/20 -125/125 [==============================] - 8s 64ms/step - loss: 1.9627 - acc: 0.3493 - val_loss: 2.2369 - val_acc: 0.2806 +125/125 [==============================] - 8s 63ms/step - loss: 0.6441 - acc: 0.7759 - val_loss: 1.0606 - val_acc: 0.6664 Epoch 10/20 -125/125 [==============================] - 8s 66ms/step - loss: 1.8266 - acc: 0.3889 - val_loss: 2.2180 - val_acc: 0.3103 +125/125 [==============================] - 8s 63ms/step - loss: 0.5409 - acc: 0.8120 - val_loss: 1.0380 - val_acc: 0.6884 Epoch 11/20 -125/125 [==============================] - 8s 67ms/step - loss: 1.6598 - acc: 0.4411 - val_loss: 2.2910 - val_acc: 0.2988 +125/125 [==============================] - 8s 65ms/step - loss: 0.4846 - acc: 0.8273 - val_loss: 1.1073 - val_acc: 0.6729 Epoch 12/20 -125/125 [==============================] - 8s 68ms/step - loss: 1.5119 - acc: 0.4961 - val_loss: 2.3962 - val_acc: 0.3083 +125/125 [==============================] - 8s 62ms/step - loss: 0.4173 - acc: 0.8553 - val_loss: 1.1256 - val_acc: 0.6864 Epoch 13/20 -125/125 [==============================] - 8s 68ms/step - loss: 1.3293 - acc: 0.5510 - val_loss: 2.6619 - val_acc: 0.2941 +125/125 [==============================] - 8s 63ms/step - loss: 0.3419 - acc: 0.8808 - val_loss: 1.1576 - val_acc: 0.6979 Epoch 14/20 -125/125 [==============================] - 9s 70ms/step - loss: 1.2461 - acc: 0.5798 - val_loss: 2.5586 - val_acc: 0.2968 +125/125 [==============================] - 8s 68ms/step - loss: 0.2869 - acc: 0.9053 - val_loss: 1.1381 - val_acc: 0.6974 Epoch 15/20 -125/125 [==============================] - 10s 76ms/step - loss: 1.0760 - acc: 0.6296 - val_loss: 2.4376 - val_acc: 0.3443 +125/125 [==============================] - 8s 67ms/step - loss: 0.2617 - acc: 0.9118 - val_loss: 1.3850 - val_acc: 0.6747 Epoch 16/20 -125/125 [==============================] - 10s 78ms/step - loss: 0.9680 - acc: 0.6772 - val_loss: 2.9347 - val_acc: 0.3233 +125/125 [==============================] - 8s 67ms/step - loss: 0.2543 - acc: 0.9152 - val_loss: 1.3119 - val_acc: 0.6972 Epoch 17/20 -125/125 [==============================] - 10s 79ms/step - loss: 0.8464 - acc: 0.7164 - val_loss: 2.4003 - val_acc: 0.3903 +125/125 [==============================] - 8s 66ms/step - loss: 0.2109 - acc: 0.9267 - val_loss: 1.3145 - val_acc: 0.6954 Epoch 18/20 -125/125 [==============================] - 9s 74ms/step - loss: 0.7512 - acc: 0.7500 - val_loss: 2.8509 - val_acc: 0.3223 +125/125 [==============================] - 8s 64ms/step - loss: 0.1939 - acc: 0.9364 - val_loss: 1.4054 - val_acc: 0.7009 Epoch 19/20 -125/125 [==============================] - 8s 68ms/step - loss: 0.6477 - acc: 0.7852 - val_loss: 3.6992 - val_acc: 0.3081 +125/125 [==============================] - 8s 67ms/step - loss: 0.1873 - acc: 0.9379 - val_loss: 1.7441 - val_acc: 0.6667 Epoch 20/20 - 71/125 [================>.............] - ETA: 3s - loss: 0.5732 - acc: 0.8188 +125/125 [==============================] - 9s 70ms/step - loss: 0.1762 - acc: 0.9420 - val_loss: 1.5269 - val_acc: 0.6927 + + ```
diff --git a/examples/nlp/md/text_classification_from_scratch.md b/examples/nlp/md/text_classification_from_scratch.md index 3838d6aa2f..a5174b9835 100644 --- a/examples/nlp/md/text_classification_from_scratch.md +++ b/examples/nlp/md/text_classification_from_scratch.md @@ -6,7 +6,7 @@ **Description:** Text sentiment classification starting from raw text files. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlpipynb/text_classification_from_scratch.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlptext_classification_from_scratch.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/text_classification_from_scratch.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlp/text_classification_from_scratch.py) @@ -42,7 +42,7 @@ Let's download the data and inspect its structure. ``` % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed -100 80.2M 100 80.2M 0 0 51.5M 0 0:00:01 0:00:01 --:--:-- 51.5M +100 80.2M 100 80.2M 0 0 45.3M 0 0:00:01 0:00:01 --:--:-- 45.3M ``` @@ -336,13 +336,13 @@ model.fit(train_ds, validation_data=val_ds, epochs=epochs)
``` Epoch 1/3 -625/625 [==============================] - 33s 52ms/step - loss: 0.6070 - accuracy: 0.6100 - val_loss: 0.3287 - val_accuracy: 0.8582 +625/625 [==============================] - 32s 51ms/step - loss: 0.6288 - accuracy: 0.5835 - val_loss: 0.3283 - val_accuracy: 0.8610 Epoch 2/3 -625/625 [==============================] - 34s 54ms/step - loss: 0.2665 - accuracy: 0.8930 - val_loss: 0.3320 - val_accuracy: 0.8676 +625/625 [==============================] - 31s 50ms/step - loss: 0.2808 - accuracy: 0.8859 - val_loss: 0.3005 - val_accuracy: 0.8796 Epoch 3/3 -625/625 [==============================] - 34s 55ms/step - loss: 0.1360 - accuracy: 0.9505 - val_loss: 0.3847 - val_accuracy: 0.8672 +625/625 [==============================] - 31s 50ms/step - loss: 0.1450 - accuracy: 0.9467 - val_loss: 0.3795 - val_accuracy: 0.8726 - + ```
@@ -356,9 +356,9 @@ model.evaluate(test_ds)
``` -782/782 [==============================] - 8s 10ms/step - loss: 0.3904 - accuracy: 0.8614 +782/782 [==============================] - 7s 9ms/step - loss: 0.3999 - accuracy: 0.8650 -[0.3903675377368927, 0.8613600134849548] +[0.39986345171928406, 0.8649600148200989] ```
@@ -389,9 +389,9 @@ end_to_end_model.evaluate(raw_test_ds)
``` -782/782 [==============================] - 13s 16ms/step - loss: 0.3903 - accuracy: 0.8608 +782/782 [==============================] - 11s 13ms/step - loss: 0.4062 - accuracy: 0.8630 -[0.39036768674850464, 0.8613600134849548] +[0.3998638987541199, 0.8649600148200989] ```
\ No newline at end of file diff --git a/examples/nlp/pretrained_word_embeddings.py b/examples/nlp/pretrained_word_embeddings.py index 5226788c18..7a62b10a32 100644 --- a/examples/nlp/pretrained_word_embeddings.py +++ b/examples/nlp/pretrained_word_embeddings.py @@ -151,7 +151,7 @@ """ voc = vectorizer.get_vocabulary() -word_index = dict(zip(voc, range(2, len(voc)))) +word_index = dict(zip(voc, range(len(voc)))) """ As you can see, we obtain the same encoding as above for our test sentence: diff --git a/examples/vision/md/super_resolution_sub_pixel.md b/examples/vision/md/super_resolution_sub_pixel.md index bdea1dfd93..17142cefc6 100644 --- a/examples/vision/md/super_resolution_sub_pixel.md +++ b/examples/vision/md/super_resolution_sub_pixel.md @@ -6,7 +6,7 @@ **Description:** Implementing Super-Resolution using Efficient sub-pixel model on BSDS500. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/visionipynb/super_resolution_sub_pixel.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/visionsuper_resolution_sub_pixel.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/super_resolution_sub_pixel.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/vision/super_resolution_sub_pixel.py) diff --git a/guides/img/functional_api/functional_api_22_0.png b/guides/img/functional_api/functional_api_22_0.png index 927b4ac39d1dbf0b292f13231121683dec24083a..4e7d432c8345e56ab6dbc918fc86ffb882828be6 100644 GIT binary patch literal 27270 zcmdSB2{@MP+CThIhESo5$t*)=l_^6qlca${WNIcd&!G~^Sdz>_gbZaSm06KU<}!s0 zkvYTvr?uC9_j=#G_q+ec@qNefea~L&*n1lu&wbt3eV)JRyzc1gsBNWSrXUaqTQ$^` z^#}wa2mE`VoETrZ*EPQz|3zx1rKU_+-}p~_S!y_ez)sLmK6JwQ@pz1z(eav33%u0v z6wHDuRRL-K#5CDtMEocB`UKDi@M+QsQ5ZaZY4F0elx16umRtVG<#>{vRg@~!)Nc)6 z&?cXu-mFY?Xs^%aL)N6U!WyDK*TifV!g>9uLt^5yrr6x)pUJt+cOIT;i0xt~t!5-5 z5SqPj72_j)$t0O2ArOiQ9z+ChVMP-HA)YeDo|B7fAT`cYFLEz|P&)d#qvL9KVW{R> z+(KkPK)~Xn^Zk;|Y2l3O9eTQBZK+(sIjYpXFs&zmTG$rNO%J zr+Eiir|R$TJz+Q5UF1u;RquW~pRDY!?25H8vChuUd(8VOIJl|F`}_Or>+7#vz51;& zD(T6Swj96ZbG}(_wL`n|KthJ?Kh$7-1!G@9pt9H^YrP(n(XU3I@_uX z3k#haABsf^8~6WQs#sh8?oY#I-CNp`LoF#;z;XCihEXaW9UiInpVHb%e6I@a4+~+s^ap#2#E)zG<&z;(RbRKeS;;*r-sqGc7ao zE_Lqn=OLfigT!3!yLs4@RK2*|e&EXIAG4#!zxElGI9?sB4ILaWb@_QJ`|J_Rvqg4K za67cO(9qCWSy@Gtn+A1tb$xpm?X}<1z+1c4`tOup+2Gu7jur_D& zFVILEo;-Qdz(C#d(}xc<6gOKU640oIUZ z8?UTvH1}Qs0VO3Rtni^jm9cVeI&tzsq3X5#!jW!+wKPGtXU=rKyxOI7_^_d&A&nA0 zKmYA*yJZ~58I}B-zOl)>FE%F}9(3@1^R=+32z%?tj~@{cJNY;4SSpqj&vc+;ks z$B$`u?06zE^*Lu?exi$U;MFU|(RrH6rY4)2!Mc_Q?HAc@l$U;=jb9&HTm7}M9r0!X z)XS?2eHwQ45&S3`mu@zXUhU@Q`OA~GnDD8gtiDJf}gZax;F z$i6t&Zer;w7m-l2Y-VMp%6zDW+M|+@mz!IZkt#hqvV|c*_ky)``Q>)uF-bN=n3SOG^GhL661EZv_V*RaL!p_wLprhTs~?ty}RBl=!q52%d6tb1PucB{X^w zxiVqjbZay(;udUYlP`Vw@@0ck@|;T5zJ2>jUFQO751rpkM)vL7x76fhXE(Q|7}-e1 zz<>a1C2W}!2gu0C{F>j~-Y)w-7+UkOiY%mlDViz2O6U!@Th8NXz=jx5D^iP zCoJ@Nv2jF%h90sY;jl0>OT_Xzx5T8RSZQe7F%WzpOQOTnKQLfyY`lN}e*T8Ka9)(a z8Vhk6C3$&yr>Wk%K|zDl&M#iPXwNoD&E0?Cz{~vnE1#d=cxQEdd1huv%P%A+$F}Kl zC{d@H_gHOXN`EwwzsW#-pe$v1Am}}KFC%}D=^>k%c~ps zjDqxmu5vj!IfuwK3f3qnDBhao#Kgq-xE*;cl~YtCEhre2`@^{urI>G2pjt{^{%ne^ zJ*qQnuo@3OFqRbkSomyd?fw1+kqX?RLA#=g)%jibi_+5rDosN)US)3|p=`cdYY|Xf zEE9I+(j_JGJIb74&oVN&%6z#ydV6&|HVq66oNs)1Xtt`Z?rB!mm0Q0e|vwgeoAWU@t&i2ki~%|Bs66*c#a-DYHmJoB>KQaU&T6hhpnxxr>E!M zQ_t8tR#sMqo8q!fugC*-uv0h2${BW!3=cc5t+-rTk(H3R{@QN~I#vBax6`Mi!oxqh zO_il;4+|4{5Ea+N$U6D>`qC(2G0|mB{ugtBWz?J3xxrPTqO>qidGlN?+s%vUQ3|~Hb@nY`dQH7yvQZ3TP zMn-Eti96^bE-sE;(;#Wx-Tdj(DP1i93*51;+EA{7D;;JRFEX*P zlwR#RkS-y+fBzh?kA@VECdhK1KSq71cZn3>1=`^B-gAtBV_Gcgen+;S!<63U#% z?CfSzjzmXfrDuh(YWqx&I)%h$Zr7#t^70ZA5(=8mJKw~-YnQB5+S8{TWO&Y699e^T z<=BN5I)z zeU^ZbP-aF(xLbNrQBhi2T0+7h7dBupBV%LBAW`|PvkpOb?~-j}`TRl0Du%<);%x0{ z!3VS6`Q4Y@7^0YF%gf8Lwk|F%^+vMJKTJot1FIg%d$>9=L`FY+7{n-+n556d#AI

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Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/distributed_training.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesdistributed_training.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/distributed_training.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/distributed_training.py) @@ -181,21 +181,16 @@ model.evaluate(test_dataset)

``` WARNING: Logging before flag parsing goes to stderr. -W0814 09:55:55.262259 4750663104 cross_device_ops.py:1202] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce. +W0829 16:54:57.025418 4592479680 cross_device_ops.py:1115] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce. Number of devices: 1 - -W0814 09:55:55.961071 4750663104 deprecation.py:323] From /usr/local/lib/python3.7/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version. -Instructions for updating: -Use `tf.data.Iterator.get_next_as_optional()` instead. - Epoch 1/2 -1563/1563 [==============================] - 3s 2ms/step - loss: 0.2290 - sparse_categorical_accuracy: 0.9323 - val_loss: 0.1623 - val_sparse_categorical_accuracy: 0.9498 +1563/1563 [==============================] - 3s 2ms/step - loss: 0.3767 - sparse_categorical_accuracy: 0.8889 - val_loss: 0.1257 - val_sparse_categorical_accuracy: 0.9623 Epoch 2/2 -1563/1563 [==============================] - 2s 1ms/step - loss: 0.0946 - sparse_categorical_accuracy: 0.9709 - val_loss: 0.1025 - val_sparse_categorical_accuracy: 0.9683 -313/313 [==============================] - 0s 802us/step - loss: 0.1029 - sparse_categorical_accuracy: 0.9677 +1563/1563 [==============================] - 2s 2ms/step - loss: 0.1053 - sparse_categorical_accuracy: 0.9678 - val_loss: 0.0944 - val_sparse_categorical_accuracy: 0.9710 +313/313 [==============================] - 0s 779us/step - loss: 0.0900 - sparse_categorical_accuracy: 0.9723 -[0.1029156744480133, 0.9677000045776367] +[0.08995261788368225, 0.9722999930381775] ```
@@ -266,15 +261,21 @@ run_training(epochs=1)
``` -W0814 09:56:01.662528 4750663104 cross_device_ops.py:1202] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce. +W0829 16:55:03.609519 4592479680 cross_device_ops.py:1115] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce. Creating a new model -1563/1563 - 3s - loss: 0.2276 - sparse_categorical_accuracy: 0.9320 - val_loss: 0.1319 - val_sparse_categorical_accuracy: 0.9579 -W0814 09:56:04.756444 4750663104 cross_device_ops.py:1202] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce. +W0829 16:55:03.708506 4592479680 callbacks.py:1270] Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback. + +1563/1563 - 4s - loss: 0.2242 - sparse_categorical_accuracy: 0.9321 - val_loss: 0.1243 - val_sparse_categorical_accuracy: 0.9647 + +W0829 16:55:07.981292 4592479680 cross_device_ops.py:1115] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce. Restoring from ./ckpt/ckpt-1 -1563/1563 - 3s - loss: 0.0976 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1005 - val_sparse_categorical_accuracy: 0.9690 + +W0829 16:55:08.245935 4592479680 callbacks.py:1270] Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback. + +1563/1563 - 4s - loss: 0.0948 - sparse_categorical_accuracy: 0.9709 - val_loss: 0.1006 - val_sparse_categorical_accuracy: 0.9699 ```
diff --git a/guides/md/functional_api.md b/guides/md/functional_api.md index 813a7a12a4..03d6c59553 100644 --- a/guides/md/functional_api.md +++ b/guides/md/functional_api.md @@ -6,7 +6,7 @@ **Description:** Complete guide to the functional API. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/functional_api.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesfunctional_api.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/functional_api.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/functional_api.py) @@ -227,12 +227,12 @@ print("Test accuracy:", test_scores[1])
``` Epoch 1/2 -750/750 [==============================] - 1s 1ms/step - loss: 0.3457 - accuracy: 0.9008 - val_loss: 0.2066 - val_accuracy: 0.9397 +750/750 [==============================] - 1s 1ms/step - loss: 0.5778 - accuracy: 0.8391 - val_loss: 0.1893 - val_accuracy: 0.9440 Epoch 2/2 -750/750 [==============================] - 1s 888us/step - loss: 0.1622 - accuracy: 0.9523 - val_loss: 0.1413 - val_accuracy: 0.9602 -313/313 - 0s - loss: 0.1371 - accuracy: 0.9586 -Test loss: 0.1370706558227539 -Test accuracy: 0.9585999846458435 +750/750 [==============================] - 1s 832us/step - loss: 0.1739 - accuracy: 0.9476 - val_loss: 0.1564 - val_accuracy: 0.9528 +313/313 - 0s - loss: 0.1497 - accuracy: 0.9528 +Test loss: 0.14971688389778137 +Test accuracy: 0.9527999758720398 ```
@@ -631,11 +631,11 @@ model.fit(
``` Epoch 1/2 -40/40 [==============================] - 1s 27ms/step - loss: 1.3267 - priority_loss: 0.6982 - department_loss: 3.1425 +40/40 [==============================] - 3s 23ms/step - loss: 1.2998 - priority_loss: 0.7066 - department_loss: 2.9655 Epoch 2/2 -40/40 [==============================] - 1s 26ms/step - loss: 1.3180 - priority_loss: 0.6966 - department_loss: 3.1070 +40/40 [==============================] - 1s 24ms/step - loss: 1.2969 - priority_loss: 0.6979 - department_loss: 2.9950 - + ```
@@ -763,9 +763,9 @@ model.fit(x_train[:1000], y_train[:1000], batch_size=64, epochs=1, validation_sp
``` -13/13 [==============================] - 1s 86ms/step - loss: 2.3197 - acc: 0.1100 - val_loss: 2.3009 - val_acc: 0.0900 +13/13 [==============================] - 2s 87ms/step - loss: 2.3145 - acc: 0.1124 - val_loss: 2.3046 - val_acc: 0.1150 - + ```
diff --git a/guides/md/intro_to_keras_for_engineers.md b/guides/md/intro_to_keras_for_engineers.md index fce5777ab6..2083e20060 100644 --- a/guides/md/intro_to_keras_for_engineers.md +++ b/guides/md/intro_to_keras_for_engineers.md @@ -6,7 +6,7 @@ **Description:** Everything you need to know to use Keras to build real-world machine learning solutions. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/intro_to_keras_for_engineers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesintro_to_keras_for_engineers.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/intro_to_keras_for_engineers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/intro_to_keras_for_engineers.py) @@ -421,7 +421,7 @@ model.summary()
``` -Model: "functional_1" +Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= @@ -541,7 +541,7 @@ history = model.fit(dataset, epochs=1)
``` -Model: "functional_3" +Model: "model_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= @@ -562,9 +562,9 @@ Trainable params: 118,282 Non-trainable params: 0 _________________________________________________________________ Fit on NumPy data -938/938 [==============================] - 1s 758us/step - loss: 0.2614 +938/938 [==============================] - 1s 940us/step - loss: 0.4771 Fit on Dataset -938/938 [==============================] - 1s 838us/step - loss: 0.1131 +938/938 [==============================] - 1s 942us/step - loss: 0.1138 ```
@@ -580,7 +580,7 @@ print(history.history)
``` -{'loss': [0.11306042224168777]} +{'loss': [0.11384169012308121]} ```
@@ -611,7 +611,7 @@ history = model.fit(dataset, epochs=1)
``` -938/938 [==============================] - 1s 875us/step - loss: 0.0775 - acc: 0.9765 +938/938 [==============================] - 1s 929us/step - loss: 0.0835 - acc: 0.9748 ```
@@ -628,7 +628,7 @@ history = model.fit(dataset, epochs=1, validation_data=val_dataset)
``` -938/938 [==============================] - 1s 1ms/step - loss: 0.0516 - acc: 0.9840 - val_loss: 0.1186 - val_acc: 0.9629 +938/938 [==============================] - 1s 1ms/step - loss: 0.0563 - acc: 0.9829 - val_loss: 0.1041 - val_acc: 0.9692 ```
@@ -713,9 +713,9 @@ print("acc: %.2f" % acc)
``` -157/157 [==============================] - 0s 647us/step - loss: 0.1186 - acc: 0.9629 -loss: 0.12 -acc: 0.96 +157/157 [==============================] - 0s 688us/step - loss: 0.1041 - acc: 0.9692 +loss: 0.10 +acc: 0.97 ```
@@ -899,9 +899,9 @@ model.fit(dataset)
``` -1/1 [==============================] - 0s 580us/step - loss: 0.4718 +1/1 [==============================] - 0s 13ms/step - loss: 0.5028 - + ```
@@ -925,9 +925,9 @@ model.fit(dataset)
``` -1/1 [==============================] - 0s 725us/step - loss: 0.4867 +1/1 [==============================] - 0s 16ms/step - loss: 0.5258 - + ```
diff --git a/guides/md/intro_to_keras_for_researchers.md b/guides/md/intro_to_keras_for_researchers.md index 0908dbf75c..1cb0461a6f 100644 --- a/guides/md/intro_to_keras_for_researchers.md +++ b/guides/md/intro_to_keras_for_researchers.md @@ -6,7 +6,7 @@ **Description:** Everything you need to know to use Keras & TF 2.0 for deep learning research. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/intro_to_keras_for_researchers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesintro_to_keras_for_researchers.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/intro_to_keras_for_researchers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/intro_to_keras_for_researchers.py) @@ -183,16 +183,16 @@ for step, (x, y) in enumerate(dataset):
``` -Step: 0 Loss: 2.4125607013702393 -Step: 100 Loss: 2.312941789627075 -Step: 200 Loss: 2.156487464904785 -Step: 300 Loss: 2.0513410568237305 -Step: 400 Loss: 1.9625763893127441 -Step: 500 Loss: 1.9079631567001343 -Step: 600 Loss: 1.8397557735443115 -Step: 700 Loss: 1.7800065279006958 -Step: 800 Loss: 1.7599058151245117 -Step: 900 Loss: 1.6182799339294434 +Step: 0 Loss: 2.441652536392212 +Step: 100 Loss: 2.147409200668335 +Step: 200 Loss: 2.0397841930389404 +Step: 300 Loss: 2.0522937774658203 +Step: 400 Loss: 1.9265646934509277 +Step: 500 Loss: 1.7808475494384766 +Step: 600 Loss: 1.7910492420196533 +Step: 700 Loss: 1.7976462841033936 +Step: 800 Loss: 1.665055274963379 +Step: 900 Loss: 1.5772807598114014 ```
@@ -351,7 +351,7 @@ print(mlp.losses) # List containing one float32 scalar
``` -[] +[] ```
@@ -410,16 +410,16 @@ for step, (x, y) in enumerate(dataset):
``` -Step: 0 Loss: 6.015680313110352 -Step: 100 Loss: 2.5813515186309814 -Step: 200 Loss: 2.436452865600586 -Step: 300 Loss: 2.3407843112945557 -Step: 400 Loss: 2.3509445190429688 -Step: 500 Loss: 2.3435847759246826 -Step: 600 Loss: 2.3450264930725098 -Step: 700 Loss: 2.323293447494507 -Step: 800 Loss: 2.3336384296417236 -Step: 900 Loss: 2.3311607837677 +Step: 0 Loss: 5.7087297439575195 +Step: 100 Loss: 2.5388927459716797 +Step: 200 Loss: 2.42716121673584 +Step: 300 Loss: 2.3531603813171387 +Step: 400 Loss: 2.364470958709717 +Step: 500 Loss: 2.3369743824005127 +Step: 600 Loss: 2.311732053756714 +Step: 700 Loss: 2.3401520252227783 +Step: 800 Loss: 2.3188178539276123 +Step: 900 Loss: 2.3179266452789307 ```
@@ -483,25 +483,25 @@ for epoch in range(2):
``` Epoch: 0 Step: 0 -Total running accuracy so far: 0.062 +Total running accuracy so far: 0.141 Epoch: 0 Step: 200 -Total running accuracy so far: 0.767 +Total running accuracy so far: 0.765 Epoch: 0 Step: 400 -Total running accuracy so far: 0.832 +Total running accuracy so far: 0.834 Epoch: 0 Step: 600 -Total running accuracy so far: 0.859 +Total running accuracy so far: 0.863 Epoch: 0 Step: 800 -Total running accuracy so far: 0.874 +Total running accuracy so far: 0.878 Epoch: 1 Step: 0 -Total running accuracy so far: 0.922 +Total running accuracy so far: 0.891 Epoch: 1 Step: 200 Total running accuracy so far: 0.940 Epoch: 1 Step: 400 Total running accuracy so far: 0.940 Epoch: 1 Step: 600 -Total running accuracy so far: 0.940 +Total running accuracy so far: 0.942 Epoch: 1 Step: 800 -Total running accuracy so far: 0.941 +Total running accuracy so far: 0.943 ```
@@ -559,16 +559,16 @@ for step, (x, y) in enumerate(dataset):
``` -Step: 0 Loss: 2.3593735694885254 -Step: 100 Loss: 0.5828534364700317 -Step: 200 Loss: 0.3395746946334839 -Step: 300 Loss: 0.19131886959075928 -Step: 400 Loss: 0.6033713817596436 -Step: 500 Loss: 0.2845451235771179 -Step: 600 Loss: 0.28691643476486206 -Step: 700 Loss: 0.20033425092697144 -Step: 800 Loss: 0.18214932084083557 -Step: 900 Loss: 0.0939672663807869 +Step: 0 Loss: 2.3350706100463867 +Step: 100 Loss: 0.5920668244361877 +Step: 200 Loss: 0.6020416617393494 +Step: 300 Loss: 0.3404780328273773 +Step: 400 Loss: 0.36501944065093994 +Step: 500 Loss: 0.30573830008506775 +Step: 600 Loss: 0.30822068452835083 +Step: 700 Loss: 0.22713921964168549 +Step: 800 Loss: 0.38135045766830444 +Step: 900 Loss: 0.0600816085934639 ```
@@ -830,17 +830,17 @@ for step, x in enumerate(dataset):
``` -Step: 0 Loss: 0.31293171644210815 -Step: 100 Loss: 0.12725294012539456 -Step: 200 Loss: 0.10037948902863175 -Step: 300 Loss: 0.09007503003179036 -Step: 400 Loss: 0.08497287925713674 -Step: 500 Loss: 0.08179647065773457 -Step: 600 Loss: 0.07938238488705503 -Step: 700 Loss: 0.07796921999261484 -Step: 800 Loss: 0.076785514082355 -Step: 900 Loss: 0.0757754919730069 -Step: 1000 Loss: 0.07482585511156371 +Step: 0 Loss: 0.35058659315109253 +Step: 100 Loss: 0.12754513588872288 +Step: 200 Loss: 0.10075780708546662 +Step: 300 Loss: 0.09039085823733149 +Step: 400 Loss: 0.0852211643744288 +Step: 500 Loss: 0.08195368597607412 +Step: 600 Loss: 0.07947637393029279 +Step: 700 Loss: 0.07809760540022319 +Step: 800 Loss: 0.07684443313884229 +Step: 900 Loss: 0.07586672588289776 +Step: 1000 Loss: 0.07490184799313068 ```
@@ -910,9 +910,9 @@ vae.fit(dataset, epochs=1)
``` -1875/1875 [==============================] - 2s 980us/step - loss: 0.0713 +1875/1875 [==============================] - 2s 1ms/step - loss: 0.0844 - + ```
@@ -1052,17 +1052,17 @@ for step, (x, y) in enumerate(dataset):
``` -Step: 0 Loss: 4.040391445159912 -Step: 100 Loss: 2.281282453991399 -Step: 200 Loss: 2.1507285257699476 -Step: 300 Loss: 2.026051885654637 -Step: 400 Loss: 1.8997635576968224 -Step: 500 Loss: 1.8468866886433606 -Step: 600 Loss: 1.8077612901252238 -Step: 700 Loss: 1.7789069544396496 -Step: 800 Loss: 1.7295975711425349 -Step: 900 Loss: 1.6740869003767394 -Step: 1000 Loss: 1.621621757313532 +Step: 0 Loss: 2.3750205039978027 +Step: 100 Loss: 2.4851158234742607 +Step: 200 Loss: 2.2262141206093244 +Step: 300 Loss: 2.01331719845159 +Step: 400 Loss: 1.913399853134537 +Step: 500 Loss: 1.7929210026391564 +Step: 600 Loss: 1.7313386243363924 +Step: 700 Loss: 1.7000203080140366 +Step: 800 Loss: 1.6227545577378495 +Step: 900 Loss: 1.5611567303581155 +Step: 1000 Loss: 1.508888113127093 ```
diff --git a/guides/md/preprocessing_layers.md b/guides/md/preprocessing_layers.md index 8cc4b40194..952d3d5334 100644 --- a/guides/md/preprocessing_layers.md +++ b/guides/md/preprocessing_layers.md @@ -6,7 +6,7 @@ **Description:** Overview of how to leverage preprocessing layers to create end-to-end models. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/preprocessing_layers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidespreprocessing_layers.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/preprocessing_layers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/preprocessing_layers.py) @@ -303,9 +303,9 @@ model.fit(x_train, y_train)
``` -1563/1563 [==============================] - 1s 753us/step - loss: 2.1225 +1563/1563 [==============================] - 1s 687us/step - loss: 2.1805 - + ```
diff --git a/guides/md/serialization_and_saving.md b/guides/md/serialization_and_saving.md index 683fd95c82..a0b4327b15 100644 --- a/guides/md/serialization_and_saving.md +++ b/guides/md/serialization_and_saving.md @@ -6,7 +6,7 @@ **Description:** Complete guide to saving & serializing models. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/serialization_and_saving.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesserialization_and_saving.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/serialization_and_saving.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/serialization_and_saving.py) @@ -130,10 +130,10 @@ reconstructed_model.fit(test_input, test_target)
``` -4/4 [==============================] - 0s 769us/step - loss: 1.5762 -4/4 [==============================] - 0s 757us/step - loss: 1.4288 +4/4 [==============================] - 0s 807us/step - loss: 0.3797 +4/4 [==============================] - 0s 776us/step - loss: 0.2899 - + ```
@@ -216,10 +216,10 @@ print("Loaded model:", loaded)
``` WARNING: Logging before flag parsing goes to stderr. -W0824 11:47:23.519001 4547915200 load.py:134] No training configuration found in save file, so the model was *not* compiled. Compile it manually. +W0829 16:56:16.310727 4621667776 load.py:133] No training configuration found in save file, so the model was *not* compiled. Compile it manually. -Original model: <__main__.CustomModel object at 0x1481ff610> -Loaded model: +Original model: <__main__.CustomModel object at 0x15464c990> +Loaded model: ```
@@ -261,10 +261,10 @@ reconstructed_model.fit(test_input, test_target)
``` -4/4 [==============================] - 0s 761us/step - loss: 0.2099 -4/4 [==============================] - 0s 700us/step - loss: 0.1912 +4/4 [==============================] - 0s 842us/step - loss: 0.2525 +4/4 [==============================] - 0s 728us/step - loss: 0.2452 - + ```
@@ -694,7 +694,7 @@ load_status.assert_consumed()
``` - + ```
@@ -854,7 +854,7 @@ Trainable params: 54,725 Non-trainable params: 0 _________________________________________________________________ - + ```
@@ -905,11 +905,11 @@ tf.train.Checkpoint(
``` -W0824 11:47:24.883770 4547915200 deprecation.py:323] From :15: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. +W0829 16:56:17.601102 4621667776 deprecation.py:323] From :15: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. Instructions for updating: Please use `layer.add_weight` method instead. - + ```
diff --git a/guides/md/training_with_built_in_methods.md b/guides/md/training_with_built_in_methods.md index 3c7582d00a..a8a996b27b 100644 --- a/guides/md/training_with_built_in_methods.md +++ b/guides/md/training_with_built_in_methods.md @@ -6,7 +6,7 @@ **Description:** Complete guide to training & evaluation with `fit()` and `evaluate()`. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/training_with_built_in_methods.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidestraining_with_built_in_methods.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/training_with_built_in_methods.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/training_with_built_in_methods.py) @@ -126,9 +126,9 @@ history = model.fit( ``` Fit model on training data Epoch 1/2 -782/782 [==============================] - 1s 1ms/step - loss: 0.3356 - sparse_categorical_accuracy: 0.9053 - val_loss: 0.1877 - val_sparse_categorical_accuracy: 0.9460 +782/782 [==============================] - 1s 1ms/step - loss: 0.5879 - sparse_categorical_accuracy: 0.8403 - val_loss: 0.1954 - val_sparse_categorical_accuracy: 0.9453 Epoch 2/2 -782/782 [==============================] - 1s 863us/step - loss: 0.1596 - sparse_categorical_accuracy: 0.9524 - val_loss: 0.1340 - val_sparse_categorical_accuracy: 0.9630 +782/782 [==============================] - 1s 824us/step - loss: 0.1732 - sparse_categorical_accuracy: 0.9496 - val_loss: 0.1287 - val_sparse_categorical_accuracy: 0.9618 ``` @@ -145,10 +145,10 @@ history.history
``` -{'loss': [0.33558389544487, 0.15955112874507904], - 'sparse_categorical_accuracy': [0.9053400158882141, 0.9524000287055969], - 'val_loss': [0.18770605325698853, 0.13403062522411346], - 'val_sparse_categorical_accuracy': [0.9459999799728394, 0.9629999995231628]} +{'loss': [0.34767386317253113, 0.1630229949951172], + 'sparse_categorical_accuracy': [0.9023799896240234, 0.951479971408844], + 'val_loss': [0.19535799324512482, 0.12865379452705383], + 'val_sparse_categorical_accuracy': [0.9452999830245972, 0.9617999792098999]} ```
@@ -171,8 +171,8 @@ print("predictions shape:", predictions.shape)
``` Evaluate on test data -79/79 [==============================] - 0s 759us/step - loss: 0.1350 - sparse_categorical_accuracy: 0.9586 -test loss, test acc: [0.1350303590297699, 0.9585999846458435] +79/79 [==============================] - 0s 736us/step - loss: 0.1365 - sparse_categorical_accuracy: 0.9590 +test loss, test acc: [0.13652312755584717, 0.9589999914169312] Generate predictions for 3 samples predictions shape: (3, 10) @@ -292,9 +292,9 @@ model.fit(x_train, y_train_one_hot, batch_size=64, epochs=1)
``` -782/782 [==============================] - 1s 712us/step - loss: 0.0160 +782/782 [==============================] - 1s 661us/step - loss: 0.0269 - + ```
@@ -336,9 +336,9 @@ model.fit(x_train, y_train_one_hot, batch_size=64, epochs=1)
``` -782/782 [==============================] - 1s 660us/step - loss: 0.0385 +782/782 [==============================] - 1s 736us/step - loss: 0.0487 - + ```
@@ -398,13 +398,13 @@ model.fit(x_train, y_train, batch_size=64, epochs=3)
``` Epoch 1/3 -782/782 [==============================] - 1s 671us/step - loss: 0.3442 - categorical_true_positives: 45101.0000 +782/782 [==============================] - 1s 739us/step - loss: 0.5528 - categorical_true_positives: 22178.0830 Epoch 2/3 -782/782 [==============================] - 1s 661us/step - loss: 0.1594 - categorical_true_positives: 47649.0000 +782/782 [==============================] - 1s 686us/step - loss: 0.1777 - categorical_true_positives: 23784.0115 Epoch 3/3 -782/782 [==============================] - 1s 690us/step - loss: 0.1148 - categorical_true_positives: 48288.0000 +782/782 [==============================] - 1s 701us/step - loss: 0.1265 - categorical_true_positives: 24122.8685 - + ```
@@ -452,9 +452,9 @@ model.fit(x_train, y_train, batch_size=64, epochs=1)
``` -782/782 [==============================] - 1s 718us/step - loss: 2.4523 +782/782 [==============================] - 1s 709us/step - loss: 3.7566 - + ```
@@ -494,9 +494,9 @@ model.fit(x_train, y_train, batch_size=64, epochs=1)
``` -782/782 [==============================] - 1s 682us/step - loss: 0.3488 - std_of_activation: 1.0204 +782/782 [==============================] - 1s 744us/step - loss: 0.5698 - std_of_activation: 0.9652 - + ```
@@ -527,9 +527,9 @@ model.fit(x_train, y_train, batch_size=64, epochs=1)
``` -782/782 [==============================] - 1s 740us/step - loss: 2.5271 - std_of_activation: 0.0020 +782/782 [==============================] - 1s 717us/step - loss: 3.3330 - std_of_activation: 0.0017 - + ```
@@ -589,9 +589,9 @@ model.fit(data)
``` -1/1 [==============================] - 0s 1ms/step - loss: 0.9967 - binary_accuracy: 0.0000e+00 +1/1 [==============================] - 0s 237ms/step - loss: 0.9813 - binary_accuracy: 0.0000e+00 - + ```
@@ -624,9 +624,9 @@ model.fit(x_train, y_train, batch_size=64, validation_split=0.2, epochs=1)
``` -625/625 [==============================] - 1s 1ms/step - loss: 0.3681 - sparse_categorical_accuracy: 0.8975 - val_loss: 0.2234 - val_sparse_categorical_accuracy: 0.9343 +625/625 [==============================] - 1s 1ms/step - loss: 0.6143 - sparse_categorical_accuracy: 0.8270 - val_loss: 0.2392 - val_sparse_categorical_accuracy: 0.9291 - + ```
@@ -676,16 +676,16 @@ dict(zip(model.metrics_names, result))
``` Epoch 1/3 -782/782 [==============================] - 1s 909us/step - loss: 0.3337 - sparse_categorical_accuracy: 0.9055 +782/782 [==============================] - 1s 985us/step - loss: 0.5597 - sparse_categorical_accuracy: 0.8473 Epoch 2/3 -782/782 [==============================] - 1s 889us/step - loss: 0.1534 - sparse_categorical_accuracy: 0.9543 +782/782 [==============================] - 1s 951us/step - loss: 0.1675 - sparse_categorical_accuracy: 0.9511 Epoch 3/3 -782/782 [==============================] - 1s 883us/step - loss: 0.1122 - sparse_categorical_accuracy: 0.9665 +782/782 [==============================] - 1s 927us/step - loss: 0.1205 - sparse_categorical_accuracy: 0.9640 Evaluate -157/157 [==============================] - 0s 670us/step - loss: 0.1155 - sparse_categorical_accuracy: 0.9664 +157/157 [==============================] - 0s 703us/step - loss: 0.1181 - sparse_categorical_accuracy: 0.9656 -{'loss': 0.11548421531915665, - 'sparse_categorical_accuracy': 0.9664000272750854} +{'loss': 0.11810935288667679, + 'sparse_categorical_accuracy': 0.9656000137329102} ```
@@ -715,13 +715,13 @@ model.fit(train_dataset, epochs=3, steps_per_epoch=100)
``` Epoch 1/3 -100/100 [==============================] - 0s 1ms/step - loss: 0.8117 - sparse_categorical_accuracy: 0.7919 +100/100 [==============================] - 0s 970us/step - loss: 1.2361 - sparse_categorical_accuracy: 0.6618 Epoch 2/3 -100/100 [==============================] - 0s 1ms/step - loss: 0.3759 - sparse_categorical_accuracy: 0.8947 +100/100 [==============================] - 0s 836us/step - loss: 0.3901 - sparse_categorical_accuracy: 0.8947 Epoch 3/3 -100/100 [==============================] - 0s 877us/step - loss: 0.3321 - sparse_categorical_accuracy: 0.8997 +100/100 [==============================] - 0s 841us/step - loss: 0.3568 - sparse_categorical_accuracy: 0.8971 - + ```
@@ -746,9 +746,9 @@ model.fit(train_dataset, epochs=1, validation_data=val_dataset)
``` -782/782 [==============================] - 1s 1ms/step - loss: 0.3275 - sparse_categorical_accuracy: 0.9073 - val_loss: 0.1734 - val_sparse_categorical_accuracy: 0.9488 +782/782 [==============================] - 1s 1ms/step - loss: 0.5799 - sparse_categorical_accuracy: 0.8372 - val_loss: 0.2000 - val_sparse_categorical_accuracy: 0.9411 - + ```
@@ -784,9 +784,9 @@ model.fit(
``` -782/782 [==============================] - 1s 971us/step - loss: 0.3408 - sparse_categorical_accuracy: 0.9029 - val_loss: 0.3079 - val_sparse_categorical_accuracy: 0.9187 +782/782 [==============================] - 1s 1ms/step - loss: 0.5742 - sparse_categorical_accuracy: 0.8404 - val_loss: 0.3442 - val_sparse_categorical_accuracy: 0.9125 - + ```
@@ -916,9 +916,9 @@ model.fit(x_train, y_train, class_weight=class_weight, batch_size=64, epochs=1)
``` Fit with class weight -782/782 [==============================] - 1s 919us/step - loss: 0.3766 - sparse_categorical_accuracy: 0.9021 +782/782 [==============================] - 1s 804us/step - loss: 0.6450 - sparse_categorical_accuracy: 0.8304 - + ```
@@ -954,9 +954,9 @@ model.fit(x_train, y_train, sample_weight=sample_weight, batch_size=64, epochs=1
``` Fit with sample weight -782/782 [==============================] - 1s 789us/step - loss: 0.3675 - sparse_categorical_accuracy: 0.9015 +782/782 [==============================] - 1s 761us/step - loss: 0.6366 - sparse_categorical_accuracy: 0.8328 - + ```
@@ -980,9 +980,9 @@ model.fit(train_dataset, epochs=1)
``` -782/782 [==============================] - 1s 996us/step - loss: 0.3692 - sparse_categorical_accuracy: 0.9039 +782/782 [==============================] - 1s 1ms/step - loss: 0.5969 - sparse_categorical_accuracy: 0.8487 - + ```
@@ -1162,10 +1162,10 @@ model.fit(
``` -4/4 [==============================] - 0s 4ms/step - loss: 10.6206 - score_output_loss: 5.9772 - class_output_loss: 4.6434 -4/4 [==============================] - 0s 3ms/step - loss: 10.1012 - score_output_loss: 5.7773 - class_output_loss: 4.3239 +4/4 [==============================] - 1s 3ms/step - loss: 12.5725 - score_output_loss: 2.5983 - class_output_loss: 9.9742 +4/4 [==============================] - 0s 4ms/step - loss: 7.8488 - score_output_loss: 1.6788 - class_output_loss: 6.1700 - + ```
@@ -1187,9 +1187,9 @@ model.fit(train_dataset, epochs=1)
``` -2/2 [==============================] - 0s 3ms/step - loss: 9.8850 - score_output_loss: 5.5676 - class_output_loss: 4.3174 +2/2 [==============================] - 0s 5ms/step - loss: 6.8673 - score_output_loss: 1.3396 - class_output_loss: 5.5278 - + ```
@@ -1240,20 +1240,24 @@ model.fit(
``` Epoch 1/20 -625/625 [==============================] - 1s 1ms/step - loss: 0.3672 - sparse_categorical_accuracy: 0.8971 - val_loss: 0.2340 - val_sparse_categorical_accuracy: 0.9299 +625/625 [==============================] - 1s 1ms/step - loss: 0.6340 - sparse_categorical_accuracy: 0.8213 - val_loss: 0.2497 - val_sparse_categorical_accuracy: 0.9260 Epoch 2/20 -625/625 [==============================] - 1s 1ms/step - loss: 0.1744 - sparse_categorical_accuracy: 0.9485 - val_loss: 0.1719 - val_sparse_categorical_accuracy: 0.9491 +625/625 [==============================] - 1s 877us/step - loss: 0.1949 - sparse_categorical_accuracy: 0.9413 - val_loss: 0.1949 - val_sparse_categorical_accuracy: 0.9420 Epoch 3/20 -625/625 [==============================] - 1s 990us/step - loss: 0.1296 - sparse_categorical_accuracy: 0.9602 - val_loss: 0.1688 - val_sparse_categorical_accuracy: 0.9485 +625/625 [==============================] - 1s 851us/step - loss: 0.1331 - sparse_categorical_accuracy: 0.9599 - val_loss: 0.1551 - val_sparse_categorical_accuracy: 0.9537 Epoch 4/20 -625/625 [==============================] - 1s 962us/step - loss: 0.1019 - sparse_categorical_accuracy: 0.9690 - val_loss: 0.1387 - val_sparse_categorical_accuracy: 0.9600 +625/625 [==============================] - 1s 917us/step - loss: 0.1028 - sparse_categorical_accuracy: 0.9702 - val_loss: 0.1397 - val_sparse_categorical_accuracy: 0.9594 Epoch 5/20 -625/625 [==============================] - 1s 934us/step - loss: 0.0842 - sparse_categorical_accuracy: 0.9740 - val_loss: 0.1315 - val_sparse_categorical_accuracy: 0.9615 +625/625 [==============================] - 1s 872us/step - loss: 0.0829 - sparse_categorical_accuracy: 0.9751 - val_loss: 0.1382 - val_sparse_categorical_accuracy: 0.9607 Epoch 6/20 -625/625 [==============================] - 1s 877us/step - loss: 0.0729 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.1369 - val_sparse_categorical_accuracy: 0.9593 -Epoch 00006: early stopping +625/625 [==============================] - 1s 858us/step - loss: 0.0704 - sparse_categorical_accuracy: 0.9796 - val_loss: 0.1272 - val_sparse_categorical_accuracy: 0.9628 +Epoch 7/20 +625/625 [==============================] - 1s 930us/step - loss: 0.0580 - sparse_categorical_accuracy: 0.9823 - val_loss: 0.1309 - val_sparse_categorical_accuracy: 0.9639 +Epoch 8/20 +625/625 [==============================] - 1s 898us/step - loss: 0.0508 - sparse_categorical_accuracy: 0.9846 - val_loss: 0.1416 - val_sparse_categorical_accuracy: 0.9624 +Epoch 00008: early stopping - + ```
@@ -1325,16 +1329,19 @@ model.fit(
``` +WARNING: Logging before flag parsing goes to stderr. +W0829 17:00:47.618874 4516810176 callbacks.py:1270] Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback. + Epoch 1/2 -621/625 [============================>.] - ETA: 0s - loss: 0.3714 - sparse_categorical_accuracy: 0.8968 -Epoch 00001: val_loss improved from inf to 0.22727, saving model to mymodel_1 -625/625 [==============================] - 1s 2ms/step - loss: 0.3700 - sparse_categorical_accuracy: 0.8972 - val_loss: 0.2273 - val_sparse_categorical_accuracy: 0.9304 +568/625 [==========================>...] - ETA: 0s - loss: 0.6382 - sparse_categorical_accuracy: 0.8266 +Epoch 00001: val_loss improved from inf to 0.21893, saving model to mymodel_1 +625/625 [==============================] - 2s 2ms/step - loss: 0.6139 - sparse_categorical_accuracy: 0.8330 - val_loss: 0.2189 - val_sparse_categorical_accuracy: 0.9341 Epoch 2/2 -615/625 [============================>.] - ETA: 0s - loss: 0.1691 - sparse_categorical_accuracy: 0.9492 -Epoch 00002: val_loss improved from 0.22727 to 0.17455, saving model to mymodel_2 -625/625 [==============================] - 1s 1ms/step - loss: 0.1688 - sparse_categorical_accuracy: 0.9492 - val_loss: 0.1746 - val_sparse_categorical_accuracy: 0.9478 +607/625 [============================>.] - ETA: 0s - loss: 0.1808 - sparse_categorical_accuracy: 0.9463 +Epoch 00002: val_loss improved from 0.21893 to 0.17071, saving model to mymodel_2 +625/625 [==============================] - 1s 2ms/step - loss: 0.1805 - sparse_categorical_accuracy: 0.9464 - val_loss: 0.1707 - val_sparse_categorical_accuracy: 0.9478 - + ```
@@ -1377,10 +1384,12 @@ model.fit(x_train, y_train, epochs=1, callbacks=callbacks)
``` +W0829 17:00:50.224370 4516810176 callbacks.py:1270] Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback. + Creating a new model -1563/1563 [==============================] - 7s 5ms/step - loss: 0.3136 - sparse_categorical_accuracy: 0.9102 +1563/1563 [==============================] - 8s 5ms/step - loss: 0.5053 - sparse_categorical_accuracy: 0.8553 - + ```
@@ -1469,7 +1478,7 @@ keras.callbacks.TensorBoard(
``` - + ```
diff --git a/guides/md/working_with_rnns.md b/guides/md/working_with_rnns.md index 247d888f9b..7179eff458 100644 --- a/guides/md/working_with_rnns.md +++ b/guides/md/working_with_rnns.md @@ -6,7 +6,7 @@ **Description:** Complete guide to using & customizing RNN layers. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/working_with_rnns.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesworking_with_rnns.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/working_with_rnns.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/working_with_rnns.py) @@ -207,7 +207,7 @@ model.summary()
``` -Model: "functional_1" +Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== @@ -512,9 +512,9 @@ model.fit(
``` -938/938 [==============================] - 10s 11ms/step - loss: 0.9289 - accuracy: 0.7052 - val_loss: 0.5966 - val_accuracy: 0.7880 +938/938 [==============================] - 14s 13ms/step - loss: 1.3533 - accuracy: 0.5564 - val_loss: 0.5511 - val_accuracy: 0.8226 - + ```
@@ -536,9 +536,9 @@ noncudnn_model.fit(
``` -938/938 [==============================] - 10s 11ms/step - loss: 0.4060 - accuracy: 0.8745 - val_loss: 0.2860 - val_accuracy: 0.9120 +938/938 [==============================] - 12s 12ms/step - loss: 0.4240 - accuracy: 0.8740 - val_loss: 0.2870 - val_accuracy: 0.9101 - + ```
@@ -697,9 +697,9 @@ model.fit(input_data, target_data, batch_size=batch_size)
``` -10/10 [==============================] - 2s 215ms/step - loss: 0.7898 - rnn_1_loss: 0.3148 - rnn_1_1_loss: 0.4750 - rnn_1_accuracy: 0.0953 - rnn_1_1_accuracy: 0.0316 +10/10 [==============================] - 3s 243ms/step - loss: 0.8848 - rnn_1_loss: 0.2835 - rnn_1_1_loss: 0.6013 - rnn_1_accuracy: 0.1175 - rnn_1_1_accuracy: 0.0328 - + ```
diff --git a/guides/md/writing_a_training_loop_from_scratch.md b/guides/md/writing_a_training_loop_from_scratch.md index 50c6211343..1644f20eff 100644 --- a/guides/md/writing_a_training_loop_from_scratch.md +++ b/guides/md/writing_a_training_loop_from_scratch.md @@ -6,7 +6,7 @@ **Description:** Complete guide to writing low-level training & evaluation loops. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/writing_a_training_loop_from_scratch.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guideswriting_a_training_loop_from_scratch.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/writing_a_training_loop_from_scratch.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/writing_a_training_loop_from_scratch.py) @@ -131,15 +131,15 @@ for epoch in range(epochs):
``` Start of epoch 0 -Training loss (for one batch) at step 0: 113.8568 +Training loss (for one batch) at step 0: 76.3562 Seen so far: 64 samples -Training loss (for one batch) at step 200: 1.8656 +Training loss (for one batch) at step 200: 1.3921 Seen so far: 12864 samples -Training loss (for one batch) at step 400: 0.9388 +Training loss (for one batch) at step 400: 1.0018 Seen so far: 25664 samples -Training loss (for one batch) at step 600: 0.5900 +Training loss (for one batch) at step 600: 0.8904 Seen so far: 38464 samples -Training loss (for one batch) at step 800: 0.6859 +Training loss (for one batch) at step 800: 0.8393 Seen so far: 51264 samples ```
@@ -147,15 +147,15 @@ Seen so far: 51264 samples
``` Start of epoch 1 -Training loss (for one batch) at step 0: 0.6952 +Training loss (for one batch) at step 0: 0.8572 Seen so far: 64 samples -Training loss (for one batch) at step 200: 0.9020 +Training loss (for one batch) at step 200: 0.7616 Seen so far: 12864 samples -Training loss (for one batch) at step 400: 0.3637 +Training loss (for one batch) at step 400: 0.8453 Seen so far: 25664 samples -Training loss (for one batch) at step 600: 0.5506 +Training loss (for one batch) at step 600: 0.4959 Seen so far: 38464 samples -Training loss (for one batch) at step 800: 0.4058 +Training loss (for one batch) at step 800: 0.9363 Seen so far: 51264 samples ``` @@ -262,38 +262,38 @@ for epoch in range(epochs):
``` Start of epoch 0 -Training loss (for one batch) at step 0: 113.7423 +Training loss (for one batch) at step 0: 134.3001 Seen so far: 64 samples -Training loss (for one batch) at step 200: 1.3798 +Training loss (for one batch) at step 200: 1.3430 Seen so far: 12864 samples -Training loss (for one batch) at step 400: 1.1403 +Training loss (for one batch) at step 400: 1.3557 Seen so far: 25664 samples -Training loss (for one batch) at step 600: 1.2430 +Training loss (for one batch) at step 600: 0.8682 Seen so far: 38464 samples -Training loss (for one batch) at step 800: 1.3247 +Training loss (for one batch) at step 800: 0.5862 Seen so far: 51264 samples -Training acc over epoch: 0.6776 -Validation acc: 0.8365 -Time taken: 4.62s +Training acc over epoch: 0.7176 +Validation acc: 0.8403 +Time taken: 4.65s ```
``` Start of epoch 1 -Training loss (for one batch) at step 0: 0.6545 +Training loss (for one batch) at step 0: 0.4264 Seen so far: 64 samples -Training loss (for one batch) at step 200: 0.5363 +Training loss (for one batch) at step 200: 0.4168 Seen so far: 12864 samples -Training loss (for one batch) at step 400: 0.7577 +Training loss (for one batch) at step 400: 0.6106 Seen so far: 25664 samples -Training loss (for one batch) at step 600: 0.9408 +Training loss (for one batch) at step 600: 0.4762 Seen so far: 38464 samples -Training loss (for one batch) at step 800: 1.0253 +Training loss (for one batch) at step 800: 0.4031 Seen so far: 51264 samples -Training acc over epoch: 0.8417 -Validation acc: 0.8878 -Time taken: 4.95s +Training acc over epoch: 0.8429 +Validation acc: 0.8774 +Time taken: 5.07s ```
@@ -384,38 +384,38 @@ for epoch in range(epochs):
``` Start of epoch 0 -Training loss (for one batch) at step 0: 1.1119 +Training loss (for one batch) at step 0: 0.6483 Seen so far: 64 samples -Training loss (for one batch) at step 200: 0.3739 +Training loss (for one batch) at step 200: 0.5966 Seen so far: 12864 samples -Training loss (for one batch) at step 400: 0.6159 +Training loss (for one batch) at step 400: 0.5951 Seen so far: 25664 samples -Training loss (for one batch) at step 600: 0.5043 +Training loss (for one batch) at step 600: 1.3830 Seen so far: 38464 samples -Training loss (for one batch) at step 800: 0.3522 +Training loss (for one batch) at step 800: 0.2758 Seen so far: 51264 samples -Training acc over epoch: 0.8737 -Validation acc: 0.9044 -Time taken: 1.12s +Training acc over epoch: 0.8756 +Validation acc: 0.8955 +Time taken: 1.18s ```
``` Start of epoch 1 -Training loss (for one batch) at step 0: 1.0275 +Training loss (for one batch) at step 0: 0.4447 Seen so far: 64 samples -Training loss (for one batch) at step 200: 0.3680 +Training loss (for one batch) at step 200: 0.3794 Seen so far: 12864 samples -Training loss (for one batch) at step 400: 0.6758 +Training loss (for one batch) at step 400: 0.4636 Seen so far: 25664 samples -Training loss (for one batch) at step 600: 0.2383 +Training loss (for one batch) at step 600: 0.3694 Seen so far: 38464 samples -Training loss (for one batch) at step 800: 0.3972 +Training loss (for one batch) at step 800: 0.2763 Seen so far: 51264 samples -Training acc over epoch: 0.8899 -Validation acc: 0.9160 -Time taken: 0.78s +Training acc over epoch: 0.8926 +Validation acc: 0.9078 +Time taken: 0.71s ```
@@ -689,7 +689,7 @@ for epoch in range(epochs): ``` Start epoch 0 discriminator loss at step 0: 0.70 -adversarial loss at step 0: 0.72 +adversarial loss at step 0: 0.68 ```
diff --git a/scripts/autogen.py b/scripts/autogen.py index 21203fc511..7d789b8675 100644 --- a/scripts/autogen.py +++ b/scripts/autogen.py @@ -134,13 +134,13 @@ def preprocess_tutobook_md_source( ' ' "[**View in Colab**](https://colab.research.google.com/github/" + github_repo_dir - + "ipynb/" + + "/ipynb/" + name + ".ipynb" + ") " '' ' ' - "[**GitHub source**](https://github.com/" + github_repo_dir + fname + ")", + "[**GitHub source**](https://github.com/" + github_repo_dir + "/" + fname + ")", "\n", ] md_content_lines = md_content_lines[:6] + button_lines + md_content_lines[6:] diff --git a/templates/getting_started/intro_to_keras_for_engineers.md b/templates/getting_started/intro_to_keras_for_engineers.md index fce5777ab6..2083e20060 100644 --- a/templates/getting_started/intro_to_keras_for_engineers.md +++ b/templates/getting_started/intro_to_keras_for_engineers.md @@ -6,7 +6,7 @@ **Description:** Everything you need to know to use Keras to build real-world machine learning solutions. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/intro_to_keras_for_engineers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesintro_to_keras_for_engineers.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/intro_to_keras_for_engineers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/intro_to_keras_for_engineers.py) @@ -421,7 +421,7 @@ model.summary()
``` -Model: "functional_1" +Model: "model" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= @@ -541,7 +541,7 @@ history = model.fit(dataset, epochs=1)
``` -Model: "functional_3" +Model: "model_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= @@ -562,9 +562,9 @@ Trainable params: 118,282 Non-trainable params: 0 _________________________________________________________________ Fit on NumPy data -938/938 [==============================] - 1s 758us/step - loss: 0.2614 +938/938 [==============================] - 1s 940us/step - loss: 0.4771 Fit on Dataset -938/938 [==============================] - 1s 838us/step - loss: 0.1131 +938/938 [==============================] - 1s 942us/step - loss: 0.1138 ```
@@ -580,7 +580,7 @@ print(history.history)
``` -{'loss': [0.11306042224168777]} +{'loss': [0.11384169012308121]} ```
@@ -611,7 +611,7 @@ history = model.fit(dataset, epochs=1)
``` -938/938 [==============================] - 1s 875us/step - loss: 0.0775 - acc: 0.9765 +938/938 [==============================] - 1s 929us/step - loss: 0.0835 - acc: 0.9748 ```
@@ -628,7 +628,7 @@ history = model.fit(dataset, epochs=1, validation_data=val_dataset)
``` -938/938 [==============================] - 1s 1ms/step - loss: 0.0516 - acc: 0.9840 - val_loss: 0.1186 - val_acc: 0.9629 +938/938 [==============================] - 1s 1ms/step - loss: 0.0563 - acc: 0.9829 - val_loss: 0.1041 - val_acc: 0.9692 ```
@@ -713,9 +713,9 @@ print("acc: %.2f" % acc)
``` -157/157 [==============================] - 0s 647us/step - loss: 0.1186 - acc: 0.9629 -loss: 0.12 -acc: 0.96 +157/157 [==============================] - 0s 688us/step - loss: 0.1041 - acc: 0.9692 +loss: 0.10 +acc: 0.97 ```
@@ -899,9 +899,9 @@ model.fit(dataset)
``` -1/1 [==============================] - 0s 580us/step - loss: 0.4718 +1/1 [==============================] - 0s 13ms/step - loss: 0.5028 - + ```
@@ -925,9 +925,9 @@ model.fit(dataset)
``` -1/1 [==============================] - 0s 725us/step - loss: 0.4867 +1/1 [==============================] - 0s 16ms/step - loss: 0.5258 - + ```
diff --git a/templates/getting_started/intro_to_keras_for_researchers.md b/templates/getting_started/intro_to_keras_for_researchers.md index 0908dbf75c..1cb0461a6f 100644 --- a/templates/getting_started/intro_to_keras_for_researchers.md +++ b/templates/getting_started/intro_to_keras_for_researchers.md @@ -6,7 +6,7 @@ **Description:** Everything you need to know to use Keras & TF 2.0 for deep learning research. - [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/intro_to_keras_for_researchers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesintro_to_keras_for_researchers.py) + [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/intro_to_keras_for_researchers.ipynb) [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/intro_to_keras_for_researchers.py) @@ -183,16 +183,16 @@ for step, (x, y) in enumerate(dataset):
``` -Step: 0 Loss: 2.4125607013702393 -Step: 100 Loss: 2.312941789627075 -Step: 200 Loss: 2.156487464904785 -Step: 300 Loss: 2.0513410568237305 -Step: 400 Loss: 1.9625763893127441 -Step: 500 Loss: 1.9079631567001343 -Step: 600 Loss: 1.8397557735443115 -Step: 700 Loss: 1.7800065279006958 -Step: 800 Loss: 1.7599058151245117 -Step: 900 Loss: 1.6182799339294434 +Step: 0 Loss: 2.441652536392212 +Step: 100 Loss: 2.147409200668335 +Step: 200 Loss: 2.0397841930389404 +Step: 300 Loss: 2.0522937774658203 +Step: 400 Loss: 1.9265646934509277 +Step: 500 Loss: 1.7808475494384766 +Step: 600 Loss: 1.7910492420196533 +Step: 700 Loss: 1.7976462841033936 +Step: 800 Loss: 1.665055274963379 +Step: 900 Loss: 1.5772807598114014 ```
@@ -351,7 +351,7 @@ print(mlp.losses) # List containing one float32 scalar
``` -[] +[] ```
@@ -410,16 +410,16 @@ for step, (x, y) in enumerate(dataset):
``` -Step: 0 Loss: 6.015680313110352 -Step: 100 Loss: 2.5813515186309814 -Step: 200 Loss: 2.436452865600586 -Step: 300 Loss: 2.3407843112945557 -Step: 400 Loss: 2.3509445190429688 -Step: 500 Loss: 2.3435847759246826 -Step: 600 Loss: 2.3450264930725098 -Step: 700 Loss: 2.323293447494507 -Step: 800 Loss: 2.3336384296417236 -Step: 900 Loss: 2.3311607837677 +Step: 0 Loss: 5.7087297439575195 +Step: 100 Loss: 2.5388927459716797 +Step: 200 Loss: 2.42716121673584 +Step: 300 Loss: 2.3531603813171387 +Step: 400 Loss: 2.364470958709717 +Step: 500 Loss: 2.3369743824005127 +Step: 600 Loss: 2.311732053756714 +Step: 700 Loss: 2.3401520252227783 +Step: 800 Loss: 2.3188178539276123 +Step: 900 Loss: 2.3179266452789307 ```
@@ -483,25 +483,25 @@ for epoch in range(2):
``` Epoch: 0 Step: 0 -Total running accuracy so far: 0.062 +Total running accuracy so far: 0.141 Epoch: 0 Step: 200 -Total running accuracy so far: 0.767 +Total running accuracy so far: 0.765 Epoch: 0 Step: 400 -Total running accuracy so far: 0.832 +Total running accuracy so far: 0.834 Epoch: 0 Step: 600 -Total running accuracy so far: 0.859 +Total running accuracy so far: 0.863 Epoch: 0 Step: 800 -Total running accuracy so far: 0.874 +Total running accuracy so far: 0.878 Epoch: 1 Step: 0 -Total running accuracy so far: 0.922 +Total running accuracy so far: 0.891 Epoch: 1 Step: 200 Total running accuracy so far: 0.940 Epoch: 1 Step: 400 Total running accuracy so far: 0.940 Epoch: 1 Step: 600 -Total running accuracy so far: 0.940 +Total running accuracy so far: 0.942 Epoch: 1 Step: 800 -Total running accuracy so far: 0.941 +Total running accuracy so far: 0.943 ```
@@ -559,16 +559,16 @@ for step, (x, y) in enumerate(dataset):
``` -Step: 0 Loss: 2.3593735694885254 -Step: 100 Loss: 0.5828534364700317 -Step: 200 Loss: 0.3395746946334839 -Step: 300 Loss: 0.19131886959075928 -Step: 400 Loss: 0.6033713817596436 -Step: 500 Loss: 0.2845451235771179 -Step: 600 Loss: 0.28691643476486206 -Step: 700 Loss: 0.20033425092697144 -Step: 800 Loss: 0.18214932084083557 -Step: 900 Loss: 0.0939672663807869 +Step: 0 Loss: 2.3350706100463867 +Step: 100 Loss: 0.5920668244361877 +Step: 200 Loss: 0.6020416617393494 +Step: 300 Loss: 0.3404780328273773 +Step: 400 Loss: 0.36501944065093994 +Step: 500 Loss: 0.30573830008506775 +Step: 600 Loss: 0.30822068452835083 +Step: 700 Loss: 0.22713921964168549 +Step: 800 Loss: 0.38135045766830444 +Step: 900 Loss: 0.0600816085934639 ```
@@ -830,17 +830,17 @@ for step, x in enumerate(dataset):
``` -Step: 0 Loss: 0.31293171644210815 -Step: 100 Loss: 0.12725294012539456 -Step: 200 Loss: 0.10037948902863175 -Step: 300 Loss: 0.09007503003179036 -Step: 400 Loss: 0.08497287925713674 -Step: 500 Loss: 0.08179647065773457 -Step: 600 Loss: 0.07938238488705503 -Step: 700 Loss: 0.07796921999261484 -Step: 800 Loss: 0.076785514082355 -Step: 900 Loss: 0.0757754919730069 -Step: 1000 Loss: 0.07482585511156371 +Step: 0 Loss: 0.35058659315109253 +Step: 100 Loss: 0.12754513588872288 +Step: 200 Loss: 0.10075780708546662 +Step: 300 Loss: 0.09039085823733149 +Step: 400 Loss: 0.0852211643744288 +Step: 500 Loss: 0.08195368597607412 +Step: 600 Loss: 0.07947637393029279 +Step: 700 Loss: 0.07809760540022319 +Step: 800 Loss: 0.07684443313884229 +Step: 900 Loss: 0.07586672588289776 +Step: 1000 Loss: 0.07490184799313068 ```
@@ -910,9 +910,9 @@ vae.fit(dataset, epochs=1)
``` -1875/1875 [==============================] - 2s 980us/step - loss: 0.0713 +1875/1875 [==============================] - 2s 1ms/step - loss: 0.0844 - + ```
@@ -1052,17 +1052,17 @@ for step, (x, y) in enumerate(dataset):
``` -Step: 0 Loss: 4.040391445159912 -Step: 100 Loss: 2.281282453991399 -Step: 200 Loss: 2.1507285257699476 -Step: 300 Loss: 2.026051885654637 -Step: 400 Loss: 1.8997635576968224 -Step: 500 Loss: 1.8468866886433606 -Step: 600 Loss: 1.8077612901252238 -Step: 700 Loss: 1.7789069544396496 -Step: 800 Loss: 1.7295975711425349 -Step: 900 Loss: 1.6740869003767394 -Step: 1000 Loss: 1.621621757313532 +Step: 0 Loss: 2.3750205039978027 +Step: 100 Loss: 2.4851158234742607 +Step: 200 Loss: 2.2262141206093244 +Step: 300 Loss: 2.01331719845159 +Step: 400 Loss: 1.913399853134537 +Step: 500 Loss: 1.7929210026391564 +Step: 600 Loss: 1.7313386243363924 +Step: 700 Loss: 1.7000203080140366 +Step: 800 Loss: 1.6227545577378495 +Step: 900 Loss: 1.5611567303581155 +Step: 1000 Loss: 1.508888113127093 ```