numpyai/__init__.py,sha256=gxHiHkedgH9csZHwbtzlfXpI3Zxu1zR5_9dYXdSaCHM,214
numpyai/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
numpyai/backend/__init__.py,sha256=sfGS58-2_-CYyP0jUs9rB3hpDAkdhwKui8GbDH9MhuQ,263
numpyai/backend/progress_bar.py,sha256=TJesNKszcWAes8fEGOOrra00uDfzPprZEUzrGRO0mh8,7659
numpyai/backend/registrable.py,sha256=473q-ZV0v1JvLUfAxzuONeCeFfqlq2nOmmLy79IM3cY,2430
numpyai/backend/representable.py,sha256=Pf7g9odmuMLPrKHv-eafRug4Cp6K-x_Rq4tB-hukV-g,830
numpyai/backend/utils.py,sha256=lwPyx_v1F2gSTcTJSP17Pa4qYJ-dvl8aZCiTBu_s8os,2223
numpyai/feature_extraction/__init__.py,sha256=9Pliqp2dlgmteLgMZInw48ACBlodNHvzrDEwKs4fYPg,88
numpyai/feature_extraction/count_vectoriser.py,sha256=NYUWirfnac67Z5PNVkdHSk6QqD0Q5-zMMiOpXqMpzSY,4442
numpyai/nn/__init__.py,sha256=EBjdpSgn55PFC0HVBJ_9XLBKRZXHxrYAZo7-_X20Khk,282
numpyai/nn/network.py,sha256=rH2BMgujv_C-lMeP7nppa5Sk-lgWEgLJfIQIEeixfw4,17147
numpyai/nn/activations/__init__.py,sha256=WhIOLebIofhGBDDvgmRvm3fwdOzzcT8TPhB26d47IQE,252
numpyai/nn/activations/activation.py,sha256=Vjx6z5t20FA2sJBbc9tLwrwMcEckmdIZ1geUNLEuW80,800
numpyai/nn/activations/leaky_relu.py,sha256=tRiFgPSoKDVP7q7-76Pob_7D3ihcyUZg3wpJw8AvpKw,741
numpyai/nn/activations/linear.py,sha256=jL5D3rM58ZgRgP3ZTXYoXheY-pUmGBCYKNQ6yzYegpA,506
numpyai/nn/activations/relu.py,sha256=8DUlVrYsdY5gmWm_PgbH7WkM6cGPVA-ao82vZdzn73U,561
numpyai/nn/activations/sigmoid.py,sha256=bZVBIetaLsOuAbL5nY54cylFBMIbRMXRoxzmjn-4NoM,575
numpyai/nn/activations/softmax.py,sha256=b7dyA4ut2yAPq4LUbO6yB4uK4SW-SuD3Zikt2nqAH_Q,614
numpyai/nn/activations/tanh.py,sha256=cuWc3LniEi6iiHt3ich-vI8aECmiHxA0dhzQUv6xTEA,541
numpyai/nn/backend/__init__.py,sha256=Zae78TtFYsbI2vcAKUlbIDuGvQ3m-lYafzzZttrMvM8,283
numpyai/nn/backend/activations.py,sha256=gLzU9FVbaCg-VU9npGH8gZkSYGZ66DnSLOdKMt9CMsU,1463
numpyai/nn/backend/losses.py,sha256=amyfSBMeK4gM2ko2Xoap1pVZcx95gjx4HfbuxqonzUM,1700
numpyai/nn/backend/regularisers.py,sha256=FU-6qsglPt-u1VuHJQ7H6zWtA-dbvT5ZWtxiSs66nHs,407
numpyai/nn/datasets/__init__.py,sha256=YvNgIz4EQdA6WoEGI9tnupi_kxoSFacbzQbaoi_27B4,101
numpyai/nn/datasets/dataset.py,sha256=wFGWvAD8qoM_dVfNP7ERchTontThbVx59-0EVjW9urM,876
numpyai/nn/datasets/mnist.py,sha256=nJm_BjultGiKoY4WoL0yUOl1peAXooj8L-i-4pxm6jo,2717
numpyai/nn/datasets/utils.py,sha256=43F3vjwPjlwYhgRI5Zv_LmTbNyJ-zQPmehCr6Qy0JSs,3973
numpyai/nn/initialisers/__init__.py,sha256=FqYPFxKdhXPV4wiyx7o51ZXwqbaFVZXhc1hUgrYu6AY,257
numpyai/nn/initialisers/constant.py,sha256=bZWEPW-sOFmqum7G5TzdlfC0RcwuCHWFe03gpVEpK0E,839
numpyai/nn/initialisers/glorot.py,sha256=A6QXFFPJefHRWfViWFF7HCKv7s4rWnHceLCcb793fj4,800
numpyai/nn/initialisers/he.py,sha256=Fxs-Bwl707AU1xuEmFzbKhZh7QXmvoQIdtOzcCo_-Nw,609
numpyai/nn/initialisers/initialiser.py,sha256=zqh-zVvuIEoAyFOfrk58o46t2hnAqugol19m2mUWZAo,713
numpyai/nn/initialisers/random.py,sha256=JFfJWHajzsTEiNMQNmsJW4CDoEu_b7T1WH-6lZnDSF0,883
numpyai/nn/layers/__init__.py,sha256=VFA9uLy_4kHsRcvNQNLpW6NPiKKuXhwvO485tVJ2OSw,302
numpyai/nn/layers/averagepooling2d.py,sha256=Tv3b1XT4VsZzCgArDlNPXjS3JiHBQCFH4enAluyiRk0,3034
numpyai/nn/layers/conv2d.py,sha256=OZQisTmNxQzbfu9gykGEIXuz7K_Q7sne_bMi0qDY8uo,6018
numpyai/nn/layers/dense.py,sha256=r-Nf034Cp1rDiJLF577X5y0yiNVQkikM6GvgE3BVGjY,3611
numpyai/nn/layers/dropout.py,sha256=2klGzvFFRR4bZ-3ilpp5AUwcQ5OrJyVcw3XS1evJ80w,1643
numpyai/nn/layers/flatten.py,sha256=o2zXi7PMHaSk8HkPuqbD6ArHx-zNxig3tvRviTdWk_Q,1025
numpyai/nn/layers/layer.py,sha256=Zor-vTI_gITQGvba1tdyX_aES21C6jIvLCrN2on5LxY,1462
numpyai/nn/layers/maxpooling2d.py,sha256=5gJovCflJ-BKUsIP7pXJTuOWiyta6e41ckZJymL3Hbk,2967
numpyai/nn/layers/trainable_layer.py,sha256=K_Pfaa090jqAa-aP-dojMtQK6Ytj8Uya9YIoAnnzhE4,1494
numpyai/nn/losses/__init__.py,sha256=jNY4I93HJ3dD03RF0QXrwEQ4fp_GEU2TNfFX-75h6h8,284
numpyai/nn/losses/binary_crossentropy.py,sha256=XKe68lgA55xV_F1G4KQa30IdMfQKqTDiFhM77nnoBt8,954
numpyai/nn/losses/categorical_crossentropy.py,sha256=t03Mi4fT9_QB-jPEWP3YF6s_zsxrvZcEpD350JMgFuA,909
numpyai/nn/losses/loss.py,sha256=aF7DgKl8IL-dwVDpdWMYJvyptVxRRFDV0e3i8vN8khU,919
numpyai/nn/losses/mean_absolute_error.py,sha256=ICjMHkEyo8RoDIMCXP0-96MaYDu1pBThKgZ6vIF02Sk,709
numpyai/nn/losses/mean_squared_error.py,sha256=De1wCXOHcYB60KU-YVsQbFA8FzqxNF7VIoGAkxaG-8U,701
numpyai/nn/metrics/__init__.py,sha256=JEUfy1OCX1PDIkJSYyxKxTEkkQrpCnxEITDJEG6dJG0,468
numpyai/nn/metrics/accuracy.py,sha256=1BPooYK_mz5j0k3WIT-MkpaEyZRP2XHkhIFHL45cbQ8,395
numpyai/nn/metrics/binary_accuracy.py,sha256=R7YV4YlugQ1hnsaALomKbpgxXYMgSq59ELm7So9smTc,706
numpyai/nn/metrics/binary_crossentropy.py,sha256=vADYvnMfP8IycUm_G0EjkgZ1c-PzYVN9oTFN13qFE8w,709
numpyai/nn/metrics/categorical_accuracy.py,sha256=KTjLAML39J6wcGNgGw2bcPpRRMCKdl5DuTDMLYy4xEo,537
numpyai/nn/metrics/categorical_crossentropy.py,sha256=1o7p8Dapv3MichHOvOlvexmVeMHZMiUeHG8CavKHrp0,745
numpyai/nn/metrics/mean_absolute_error.py,sha256=B0wyeTWjEC2WDT55OtpDAkLnCYQp9IsPwKujC_1aOHs,489
numpyai/nn/metrics/mean_squared_error.py,sha256=xGSH5ls8wlll7hLSHQ0nsDUMqFqlynah2skT_pcNHgo,481
numpyai/nn/metrics/metric.py,sha256=55oncmKoeTEBJBjw_13CAR36-EIA-2CuvS_WvCZx4ik,947
numpyai/nn/metrics/root_mean_squared_error.py,sha256=Qqn4S3G9IPf3uU1DXUPHQ-Yqr0IR729Jj1zof6rw2tk,541
numpyai/nn/optimisers/__init__.py,sha256=74pjXGZMwbwOpEyuD11swMdukMLAou4vrxV-vNN-E9Q,286
numpyai/nn/optimisers/adadelta.py,sha256=3ycW18PMWmfI9V_6lVtltbPIomcOg40EqidRF8SYzIQ,1575
numpyai/nn/optimisers/adagrad.py,sha256=IlEhz-55A79LZQEJkLIxLPTwm8z_2pExqROg3-w_kvk,1701
numpyai/nn/optimisers/adam.py,sha256=klNXb0r4GEAhu-j-0Q29_lf57Q7B0KWUWGvlCJ7cb3c,1958
numpyai/nn/optimisers/adamax.py,sha256=q-TOGnp9RC68xK1mSvydBB_eIljLBcgGZv5jLULt7Bc,1567
numpyai/nn/optimisers/adamw.py,sha256=vltD2v-P419Dtg4bhW0NfZiZiGJRPyF8LzYgXYVeijc,2152
numpyai/nn/optimisers/nadam.py,sha256=46LkEMFKzsgeB5fJYACu6LpV0-fMvPexWFar_95NkOU,2094
numpyai/nn/optimisers/optimiser.py,sha256=Q55dSyDxmFyXCWYBuK45-lu-nPue32DvCYVaSSg_qRI,1299
numpyai/nn/optimisers/rmsprop.py,sha256=EpTE4jlnZklqDrN_CbTHn-FwZx5xE4FGVSxg_Z6vBck,1240
numpyai/nn/optimisers/sgd.py,sha256=6DJxs60_NJMCL85faiQdzqJa36pNb5lV0YcGwb-R6LE,1337
numpyai/nn/regularisers/__init__.py,sha256=lH3v5j2bn8tm_LNSEWI_SuPIK_VZmhWQbXd6K0SBfGs,142
numpyai/nn/regularisers/l1.py,sha256=Px_cG0RXvKRW2F-aOGYDY_JyJ541Rvy8rFA-63UH4Qw,657
numpyai/nn/regularisers/l1l2.py,sha256=4pjdLyGGQf1sNqgDtH4XDdLPw6UVr_cHFw_DnV8KG1A,809
numpyai/nn/regularisers/l2.py,sha256=QInU-2Di4zb0ccmwKTNmBfbxK5RJUGpqPYs5F7U-_vY,652
numpyai/nn/regularisers/regulariser.py,sha256=aEI-TS9GfwzNdEmbWTNCRm15SOMDcEOvKwiIk2G61tI,789
numpyai/preprocessing/__init__.py,sha256=Jij-KC7aInhUKBi3eBj5knmuB2AoFVsaCu08QSdas1E,142
numpyai/preprocessing/label_binariser.py,sha256=XJdstA1BwoYekx6_8LSphsrlXh2QBZXNFBNvcki8LSk,4478
numpyai/preprocessing/ordinal_encoder.py,sha256=GZfeQDYw_Z_2mMszu6_UuoBkXfXpwIQCI1DgwYeSYBM,4432
numpyai/supervised/__init__.py,sha256=19zKMaBqugekUSkxD4_DeDxw6txDs1NqTREmb_Sf4x4,69
numpyai/supervised/naive_bayes/__init__.py,sha256=_4tvRsumLKAmlO-29Vgj3-VsDf4u1DkhZVOsHURCmCY,248
numpyai/supervised/naive_bayes/base.py,sha256=xmo0U7xq5eCSofSfGlpPna8lv8UkZRk_Gav6JCAFFY8,2968
numpyai/supervised/naive_bayes/bernoulli.py,sha256=2KPUu5z0aCME8mmCihZWr5yEpJ0Bss0HUKIx97_9h94,2802
numpyai/supervised/naive_bayes/categorical.py,sha256=nh5OIzYhNqgFhsP-ESNzcDuZeQLwSCIyEQ2IlO8eyPQ,3666
numpyai/supervised/naive_bayes/complement.py,sha256=k2tgJTlfobG8MaJanwbRE74HtbENOPSuQpImGvi7qr8,2810
numpyai/supervised/naive_bayes/gaussian.py,sha256=VnHhR--2mpbdUdfGFmZal8I5W_yhrSzLClCDeMC-Iak,2767
numpyai/supervised/naive_bayes/multinomial.py,sha256=GkSt9_3npJKV4C3ZK8RjEFZirvNhRzJ3QNqLb7NM1cY,2184
numpy_ai_kit-0.1.0.dist-info/METADATA,sha256=Hxm5GdXN4qAmJZcFRWl757Mm_W2caV7KQp30ezLAMS0,908
numpy_ai_kit-0.1.0.dist-info/WHEEL,sha256=WLgqFyCfm_KASv4WHyYy0P3pM_m7J5L9k2skdKLirC8,87
numpy_ai_kit-0.1.0.dist-info/licenses/LICENSE,sha256=EDvBB4EROyVRCBcDgevSf83ECcnPuT3KAuhnQRxFr2c,1070
numpy_ai_kit-0.1.0.dist-info/RECORD,,
