From c37397416f53458ae5afd67fb35d7abd6e08513c Mon Sep 17 00:00:00 2001 From: cerlymarco <36955807+cerlymarco@users.noreply.github.com> Date: Wed, 5 May 2021 15:59:22 +0200 Subject: [PATCH] Release --- notebooks/Advance Usage.ipynb | 168 ++++--- notebooks/Basic Usage GridSearch.ipynb | 92 ++-- notebooks/Basic Usage RandomSearch.ipynb | 432 ++++++++++-------- .../Multi-Input Multi-Output Search.ipynb | 192 ++++---- 4 files changed, 501 insertions(+), 383 deletions(-) diff --git a/notebooks/Advance Usage.ipynb b/notebooks/Advance Usage.ipynb index 5e18c50..b51d17c 100644 --- a/notebooks/Advance Usage.ipynb +++ b/notebooks/Advance Usage.ipynb @@ -174,6 +174,16 @@ "Epoch 00041: early stopping\n", "SCORE: 0.94431 at epoch 31\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -294,6 +304,16 @@ "Epoch 00046: early stopping\n", "SCORE: 0.9406 at epoch 36\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -367,53 +387,63 @@ "8 trials detected for ('unit', 'kernel', 'lr', 'layer_types', 'epochs', 'batch_size')\n", "\n", "***** (1/8) *****\n", - "Search({'unit': 32, 'kernel': 3, 'lr': 0.1, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n", - "Restoring model weights from the end of the best epoch.\n", - "Epoch 00020: early stopping\n", - "SCORE: 0.92913 at epoch 18\n", - "\n", - "***** (2/8) *****\n", "Search({'unit': 32, 'kernel': 3, 'lr': 0.1, 'layer_types': 'pool', 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00008: early stopping\n", "SCORE: 0.108 at epoch 3\n", "\n", - "***** (3/8) *****\n", - "Search({'unit': 32, 'kernel': 3, 'lr': 0.01, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n", + "***** (2/8) *****\n", + "Search({'unit': 32, 'kernel': 3, 'lr': 0.1, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00013: early stopping\n", - "SCORE: 0.96423 at epoch 13\n", + "Epoch 00020: early stopping\n", + "SCORE: 0.92913 at epoch 18\n", "\n", - "***** (4/8) *****\n", + "***** (3/8) *****\n", "Search({'unit': 32, 'kernel': 3, 'lr': 0.01, 'layer_types': 'pool', 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00040: early stopping\n", "SCORE: 0.71785 at epoch 35\n", "\n", - "***** (5/8) *****\n", - "Search({'unit': 64, 'kernel': 3, 'lr': 0.1, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n", + "***** (4/8) *****\n", + "Search({'unit': 32, 'kernel': 3, 'lr': 0.01, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00013: early stopping\n", - "SCORE: 0.8866 at epoch 9\n", + "SCORE: 0.96423 at epoch 13\n", "\n", - "***** (6/8) *****\n", + "***** (5/8) *****\n", "Search({'unit': 64, 'kernel': 3, 'lr': 0.1, 'layer_types': 'pool', 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00006: early stopping\n", "SCORE: 0.1134 at epoch 1\n", "\n", - "***** (7/8) *****\n", - "Search({'unit': 64, 'kernel': 3, 'lr': 0.01, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n", + "***** (6/8) *****\n", + "Search({'unit': 64, 'kernel': 3, 'lr': 0.1, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00012: early stopping\n", - "SCORE: 0.96625 at epoch 8\n", + "Epoch 00013: early stopping\n", + "SCORE: 0.8866 at epoch 9\n", "\n", - "***** (8/8) *****\n", + "***** (7/8) *****\n", "Search({'unit': 64, 'kernel': 3, 'lr': 0.01, 'layer_types': 'pool', 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00054: early stopping\n", - "SCORE: 0.77354 at epoch 49\n" + "SCORE: 0.77354 at epoch 49\n", + "\n", + "***** (8/8) *****\n", + "Search({'unit': 64, 'kernel': 3, 'lr': 0.01, 'layer_types': 'flat', 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00012: early stopping\n", + "SCORE: 0.96625 at epoch 8\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -491,101 +521,111 @@ "16 trials detected for ('unit_1', 'unit_2', 'opt', 'lr', 'epochs', 'batch_size')\n", "\n", "***** (1/16) *****\n", - "Search({'unit_1': 128, 'unit_2': 64, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", - "Restoring model weights from the end of the best epoch.\n", - "Epoch 00016: early stopping\n", - "SCORE: 0.95174 at epoch 13\n", - "\n", - "***** (2/16) *****\n", - "Search({'unit_1': 128, 'unit_2': 64, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", - "Restoring model weights from the end of the best epoch.\n", - "Epoch 00033: early stopping\n", - "SCORE: 0.95005 at epoch 28\n", - "\n", - "***** (3/16) *****\n", "Search({'unit_1': 128, 'unit_2': 64, 'opt': 'nadam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00012: early stopping\n", "SCORE: 0.95073 at epoch 7\n", "\n", - "***** (4/16) *****\n", + "***** (2/16) *****\n", "Search({'unit_1': 128, 'unit_2': 64, 'opt': 'nadam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00030: early stopping\n", "SCORE: 0.94938 at epoch 30\n", "\n", - "***** (5/16) *****\n", - "Search({'unit_1': 128, 'unit_2': 32, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", + "***** (3/16) *****\n", + "Search({'unit_1': 128, 'unit_2': 64, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00019: early stopping\n", - "SCORE: 0.9514 at epoch 14\n", + "Epoch 00016: early stopping\n", + "SCORE: 0.95174 at epoch 13\n", "\n", - "***** (6/16) *****\n", - "Search({'unit_1': 128, 'unit_2': 32, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", + "***** (4/16) *****\n", + "Search({'unit_1': 128, 'unit_2': 64, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00029: early stopping\n", - "SCORE: 0.94533 at epoch 24\n", + "Epoch 00033: early stopping\n", + "SCORE: 0.95005 at epoch 28\n", "\n", - "***** (7/16) *****\n", + "***** (5/16) *****\n", "Search({'unit_1': 128, 'unit_2': 32, 'opt': 'nadam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00015: early stopping\n", "SCORE: 0.94668 at epoch 10\n", "\n", - "***** (8/16) *****\n", + "***** (6/16) *****\n", "Search({'unit_1': 128, 'unit_2': 32, 'opt': 'nadam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00027: early stopping\n", "SCORE: 0.94398 at epoch 22\n", "\n", - "***** (9/16) *****\n", - "Search({'unit_1': 64, 'unit_2': 64, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", + "***** (7/16) *****\n", + "Search({'unit_1': 128, 'unit_2': 32, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00019: early stopping\n", - "SCORE: 0.95073 at epoch 14\n", + "SCORE: 0.9514 at epoch 14\n", "\n", - "***** (10/16) *****\n", - "Search({'unit_1': 64, 'unit_2': 64, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", + "***** (8/16) *****\n", + "Search({'unit_1': 128, 'unit_2': 32, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00029: early stopping\n", - "SCORE: 0.93824 at epoch 28\n", + "SCORE: 0.94533 at epoch 24\n", "\n", - "***** (11/16) *****\n", + "***** (9/16) *****\n", "Search({'unit_1': 64, 'unit_2': 64, 'opt': 'nadam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00019: early stopping\n", "SCORE: 0.94701 at epoch 17\n", "\n", - "***** (12/16) *****\n", + "***** (10/16) *****\n", "Search({'unit_1': 64, 'unit_2': 64, 'opt': 'nadam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00035: early stopping\n", "SCORE: 0.9406 at epoch 34\n", "\n", - "***** (13/16) *****\n", - "Search({'unit_1': 64, 'unit_2': 32, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", + "***** (11/16) *****\n", + "Search({'unit_1': 64, 'unit_2': 64, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00023: early stopping\n", - "SCORE: 0.9487 at epoch 18\n", + "Epoch 00019: early stopping\n", + "SCORE: 0.95073 at epoch 14\n", "\n", - "***** (14/16) *****\n", - "Search({'unit_1': 64, 'unit_2': 32, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", + "***** (12/16) *****\n", + "Search({'unit_1': 64, 'unit_2': 64, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00042: early stopping\n", - "SCORE: 0.93925 at epoch 37\n", + "Epoch 00029: early stopping\n", + "SCORE: 0.93824 at epoch 28\n", "\n", - "***** (15/16) *****\n", + "***** (13/16) *****\n", "Search({'unit_1': 64, 'unit_2': 32, 'opt': 'nadam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00017: early stopping\n", "SCORE: 0.94735 at epoch 12\n", "\n", - "***** (16/16) *****\n", + "***** (14/16) *****\n", "Search({'unit_1': 64, 'unit_2': 32, 'opt': 'nadam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00027: early stopping\n", - "SCORE: 0.93486 at epoch 22\n" + "SCORE: 0.93486 at epoch 22\n", + "\n", + "***** (15/16) *****\n", + "Search({'unit_1': 64, 'unit_2': 32, 'opt': 'adam', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00023: early stopping\n", + "SCORE: 0.9487 at epoch 18\n", + "\n", + "***** (16/16) *****\n", + "Search({'unit_1': 64, 'unit_2': 32, 'opt': 'adam', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00042: early stopping\n", + "SCORE: 0.93925 at epoch 37\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -614,7 +654,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/notebooks/Basic Usage GridSearch.ipynb b/notebooks/Basic Usage GridSearch.ipynb index 5b4bfdb..96e9c43 100644 --- a/notebooks/Basic Usage GridSearch.ipynb +++ b/notebooks/Basic Usage GridSearch.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -47,7 +47,7 @@ "((6036, 28, 28), (6036,), (2963, 28, 28), (2963,))" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -179,7 +179,7 @@ "" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -207,7 +207,7 @@ "[0.95208, 0.95005, 0.9514, 0.95039, 0.95343, 0.94398, 0.9487, 0.93993]" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -269,7 +269,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -290,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -316,51 +316,61 @@ "***** (1/8) *****\n", "Search({'unit_1': 128, 'unit_2': 64, 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00039: early stopping\n", - "SCORE: 0.8947 at epoch 34\n", + "Epoch 00029: early stopping\n", + "SCORE: 0.89436 at epoch 24\n", "\n", "***** (2/8) *****\n", "Search({'unit_1': 128, 'unit_2': 64, 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00045: early stopping\n", - "SCORE: 0.87816 at epoch 40\n", + "Epoch 00039: early stopping\n", + "SCORE: 0.87884 at epoch 39\n", "\n", "***** (3/8) *****\n", "Search({'unit_1': 128, 'unit_2': 32, 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00057: early stopping\n", - "SCORE: 0.90888 at epoch 52\n", + "Epoch 00046: early stopping\n", + "SCORE: 0.90179 at epoch 41\n", "\n", "***** (4/8) *****\n", "Search({'unit_1': 128, 'unit_2': 32, 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00034: early stopping\n", - "SCORE: 0.8731 at epoch 29\n", + "Epoch 00031: early stopping\n", + "SCORE: 0.86196 at epoch 26\n", "\n", "***** (5/8) *****\n", "Search({'unit_1': 64, 'unit_2': 64, 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00015: early stopping\n", - "SCORE: 0.81674 at epoch 10\n", + "Epoch 00030: early stopping\n", + "SCORE: 0.8596 at epoch 25\n", "\n", "***** (6/8) *****\n", "Search({'unit_1': 64, 'unit_2': 64, 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00045: early stopping\n", - "SCORE: 0.84509 at epoch 40\n", + "Epoch 00034: early stopping\n", + "SCORE: 0.83496 at epoch 29\n", "\n", "***** (7/8) *****\n", "Search({'unit_1': 64, 'unit_2': 32, 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00024: early stopping\n", - "SCORE: 0.82855 at epoch 19\n", + "Epoch 00025: early stopping\n", + "SCORE: 0.83733 at epoch 20\n", "\n", "***** (8/8) *****\n", "Search({'unit_1': 64, 'unit_2': 32, 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00036: early stopping\n", - "SCORE: 0.82889 at epoch 31\n" + "Epoch 00048: early stopping\n", + "SCORE: 0.85218 at epoch 43\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -385,7 +395,7 @@ { "data": { "text/plain": [ - "[0.8947, 0.87816, 0.90888, 0.8731, 0.81674, 0.84509, 0.82855, 0.82889]" + "[0.89436, 0.87884, 0.90179, 0.86196, 0.8596, 0.83496, 0.83733, 0.85218]" ] }, "execution_count": 13, @@ -405,7 +415,7 @@ { "data": { "text/plain": [ - "0.90888" + "0.90179" ] }, "execution_count": 14, @@ -428,7 +438,7 @@ "{'unit_1': 128,\n", " 'unit_2': 32,\n", " 'lr': 0.01,\n", - " 'epochs': 52,\n", + " 'epochs': 41,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 11}" ] @@ -450,7 +460,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -664,6 +674,16 @@ "Epoch 00041: early stopping\n", "SCORE: 0.93965 at epoch 36\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -775,9 +795,9 @@ { "data": { "text/plain": [ - "{'fold 1': ,\n", - " 'fold 2': ,\n", - " 'fold 3': }" + "{'fold 1': ,\n", + " 'fold 2': ,\n", + " 'fold 3': }" ] }, "execution_count": 22, @@ -863,7 +883,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/notebooks/Basic Usage RandomSearch.ipynb b/notebooks/Basic Usage RandomSearch.ipynb index eee4c68..1e4f45d 100644 --- a/notebooks/Basic Usage RandomSearch.ipynb +++ b/notebooks/Basic Usage RandomSearch.ipynb @@ -126,93 +126,105 @@ "15 trials detected for ('unit_1', 'unit_2', 'lr', 'epochs', 'batch_size')\n", "\n", "***** (1/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 52, 'lr': 0.0005565835702925923, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 61, 'lr': 0.00026941073027491154, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00061: early stopping\n", - "SCORE: 0.94634 at epoch 55\n", + "Epoch 00067: early stopping\n", + "SCORE: 0.93453 at epoch 62\n", "\n", "***** (2/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 65, 'lr': 0.00013052239753580174, 'epochs': 100, 'batch_size': 512})\n", - "SCORE: 0.92845 at epoch 100\n", + "Search({'unit_1': 128, 'unit_2': 105, 'lr': 0.018917299504794916, 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00041: early stopping\n", + "SCORE: 0.94836 at epoch 31\n", "\n", "***** (3/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 105, 'lr': 0.0023668178974410024, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 81, 'lr': 0.006387142993161844, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00040: early stopping\n", - "SCORE: 0.93858 at epoch 30\n", + "Epoch 00027: early stopping\n", + "SCORE: 0.95714 at epoch 24\n", "\n", "***** (4/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 37, 'lr': 0.015329273569045341, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 96, 'lr': 0.00016980298333942208, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00030: early stopping\n", - "SCORE: 0.95444 at epoch 20\n", + "Epoch 00076: early stopping\n", + "SCORE: 0.93149 at epoch 76\n", "\n", "***** (5/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 47, 'lr': 0.0681537581148546, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 125, 'lr': 0.00031100502312585046, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00028: early stopping\n", - "SCORE: 0.90753 at epoch 23\n", + "Epoch 00060: early stopping\n", + "SCORE: 0.94465 at epoch 53\n", "\n", "***** (6/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 65, 'lr': 0.0014266563000201306, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 38, 'lr': 0.008604539745472692, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00031: early stopping\n", - "SCORE: 0.93824 at epoch 21\n", + "Epoch 00029: early stopping\n", + "SCORE: 0.95174 at epoch 19\n", "\n", "***** (7/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 41, 'lr': 0.004371872304807245, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 67, 'lr': 0.048643553143575324, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00025: early stopping\n", - "SCORE: 0.94803 at epoch 21\n", + "Epoch 00024: early stopping\n", + "SCORE: 0.92575 at epoch 14\n", "\n", "***** (8/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 102, 'lr': 0.0016713256725139364, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 72, 'lr': 0.028446264284627223, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00042: early stopping\n", - "SCORE: 0.94533 at epoch 32\n", + "Epoch 00022: early stopping\n", + "SCORE: 0.93453 at epoch 12\n", "\n", "***** (9/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 96, 'lr': 0.0005659406265053385, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 79, 'lr': 0.0002597559131018589, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00074: early stopping\n", - "SCORE: 0.95039 at epoch 64\n", + "Epoch 00085: early stopping\n", + "SCORE: 0.93284 at epoch 84\n", "\n", "***** (10/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 83, 'lr': 0.0013292918943162162, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 89, 'lr': 0.027450030060679365, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00034: early stopping\n", - "SCORE: 0.94094 at epoch 29\n", + "Epoch 00022: early stopping\n", + "SCORE: 0.9406 at epoch 12\n", "\n", "***** (11/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 100, 'lr': 0.00022139290514335152, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 77, 'lr': 0.011180289095021183, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00085: early stopping\n", - "SCORE: 0.93925 at epoch 81\n", + "Epoch 00027: early stopping\n", + "SCORE: 0.95174 at epoch 17\n", "\n", "***** (12/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 52, 'lr': 0.031954089406218945, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 111, 'lr': 0.000289005574642862, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00020: early stopping\n", - "SCORE: 0.93689 at epoch 10\n", + "Epoch 00077: early stopping\n", + "SCORE: 0.93858 at epoch 67\n", "\n", "***** (13/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 107, 'lr': 0.09269035391921808, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 89, 'lr': 0.04442377859898533, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00059: early stopping\n", - "SCORE: 0.77759 at epoch 49\n", + "Epoch 00018: early stopping\n", + "SCORE: 0.91731 at epoch 8\n", "\n", "***** (14/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 93, 'lr': 0.02246451529138593, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 69, 'lr': 0.0026994807935171675, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00032: early stopping\n", - "SCORE: 0.94398 at epoch 25\n", + "Epoch 00034: early stopping\n", + "SCORE: 0.94296 at epoch 24\n", "\n", "***** (15/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 39, 'lr': 0.00021901074155610388, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 32, 'lr': 0.07961407194405414, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00062: early stopping\n", - "SCORE: 0.9379 at epoch 54\n" + "Epoch 00026: early stopping\n", + "SCORE: 0.90786 at epoch 16\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -236,21 +248,21 @@ { "data": { "text/plain": [ - "[0.94634,\n", - " 0.92845,\n", + "[0.93453,\n", + " 0.94836,\n", + " 0.95714,\n", + " 0.93149,\n", + " 0.94465,\n", + " 0.95174,\n", + " 0.92575,\n", + " 0.93453,\n", + " 0.93284,\n", + " 0.9406,\n", + " 0.95174,\n", " 0.93858,\n", - " 0.95444,\n", - " 0.90753,\n", - " 0.93824,\n", - " 0.94803,\n", - " 0.94533,\n", - " 0.95039,\n", - " 0.94094,\n", - " 0.93925,\n", - " 0.93689,\n", - " 0.77759,\n", - " 0.94398,\n", - " 0.9379]" + " 0.91731,\n", + " 0.94296,\n", + " 0.90786]" ] }, "execution_count": 7, @@ -270,7 +282,7 @@ { "data": { "text/plain": [ - "0.95444" + "0.95714" ] }, "execution_count": 8, @@ -291,9 +303,9 @@ "data": { "text/plain": [ "{'unit_1': 128,\n", - " 'unit_2': 37,\n", - " 'lr': 0.015329273569045341,\n", - " 'epochs': 20,\n", + " 'unit_2': 81,\n", + " 'lr': 0.006387142993161844,\n", + " 'epochs': 24,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 12}" ] @@ -315,7 +327,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -360,93 +372,105 @@ "15 trials detected for ('unit_1', 'unit_2', 'lr', 'epochs', 'batch_size')\n", "\n", "***** (1/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 52, 'lr': 0.0005565835702925923, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 61, 'lr': 0.00026941073027491154, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00061: early stopping\n", - "SCORE: 0.8785 at epoch 56\n", + "Epoch 00086: early stopping\n", + "SCORE: 0.82551 at epoch 82\n", "\n", "***** (2/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 65, 'lr': 0.00013052239753580174, 'epochs': 100, 'batch_size': 512})\n", - "SCORE: 0.77725 at epoch 99\n", + "Search({'unit_1': 128, 'unit_2': 105, 'lr': 0.018917299504794916, 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00039: early stopping\n", + "SCORE: 0.86061 at epoch 34\n", "\n", "***** (3/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 105, 'lr': 0.0023668178974410024, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 81, 'lr': 0.006387142993161844, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00040: early stopping\n", - "SCORE: 0.87108 at epoch 35\n", + "Epoch 00019: early stopping\n", + "SCORE: 0.86736 at epoch 14\n", "\n", "***** (4/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 37, 'lr': 0.015329273569045341, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 96, 'lr': 0.00016980298333942208, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00038: early stopping\n", - "SCORE: 0.87344 at epoch 33\n", + "Epoch 00092: early stopping\n", + "SCORE: 0.80628 at epoch 87\n", "\n", "***** (5/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 47, 'lr': 0.0681537581148546, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 125, 'lr': 0.00031100502312585046, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00017: early stopping\n", - "SCORE: 0.32973 at epoch 12\n", + "Epoch 00051: early stopping\n", + "SCORE: 0.85116 at epoch 46\n", "\n", "***** (6/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 65, 'lr': 0.0014266563000201306, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 38, 'lr': 0.008604539745472692, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00042: early stopping\n", - "SCORE: 0.8542 at epoch 37\n", + "Epoch 00014: early stopping\n", + "SCORE: 0.83631 at epoch 9\n", "\n", "***** (7/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 41, 'lr': 0.004371872304807245, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 67, 'lr': 0.048643553143575324, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00029: early stopping\n", - "SCORE: 0.8974 at epoch 24\n", + "Epoch 00017: early stopping\n", + "SCORE: 0.54877 at epoch 12\n", "\n", "***** (8/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 102, 'lr': 0.0016713256725139364, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 72, 'lr': 0.028446264284627223, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00035: early stopping\n", - "SCORE: 0.85555 at epoch 30\n", + "Epoch 00021: early stopping\n", + "SCORE: 0.75937 at epoch 16\n", "\n", "***** (9/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 96, 'lr': 0.0005659406265053385, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 79, 'lr': 0.0002597559131018589, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00031: early stopping\n", - "SCORE: 0.84813 at epoch 26\n", + "Epoch 00065: early stopping\n", + "SCORE: 0.79447 at epoch 63\n", "\n", "***** (10/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 83, 'lr': 0.0013292918943162162, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 89, 'lr': 0.027450030060679365, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00032: early stopping\n", - "SCORE: 0.84475 at epoch 27\n", + "Epoch 00021: early stopping\n", + "SCORE: 0.77827 at epoch 16\n", "\n", "***** (11/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 100, 'lr': 0.00022139290514335152, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 77, 'lr': 0.011180289095021183, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00053: early stopping\n", - "SCORE: 0.784 at epoch 50\n", + "Epoch 00032: early stopping\n", + "SCORE: 0.85218 at epoch 27\n", "\n", "***** (12/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 52, 'lr': 0.031954089406218945, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 111, 'lr': 0.000289005574642862, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00021: early stopping\n", - "SCORE: 0.69389 at epoch 16\n", + "Epoch 00092: early stopping\n", + "SCORE: 0.84846 at epoch 87\n", "\n", "***** (13/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 107, 'lr': 0.09269035391921808, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 89, 'lr': 0.04442377859898533, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00015: early stopping\n", - "SCORE: 0.15829 at epoch 10\n", + "Epoch 00017: early stopping\n", + "SCORE: 0.61323 at epoch 12\n", "\n", "***** (14/15) *****\n", - "Search({'unit_1': 64, 'unit_2': 93, 'lr': 0.02246451529138593, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 69, 'lr': 0.0026994807935171675, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00035: early stopping\n", - "SCORE: 0.83598 at epoch 30\n", + "Epoch 00019: early stopping\n", + "SCORE: 0.8245 at epoch 14\n", "\n", "***** (15/15) *****\n", - "Search({'unit_1': 128, 'unit_2': 39, 'lr': 0.00021901074155610388, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 32, 'lr': 0.07961407194405414, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00067: early stopping\n", - "SCORE: 0.84543 at epoch 62\n" + "Epoch 00016: early stopping\n", + "SCORE: 0.31792 at epoch 11\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -472,21 +496,21 @@ { "data": { "text/plain": [ - "[0.8785,\n", - " 0.77725,\n", - " 0.87108,\n", - " 0.87344,\n", - " 0.32973,\n", - " 0.8542,\n", - " 0.8974,\n", - " 0.85555,\n", - " 0.84813,\n", - " 0.84475,\n", - " 0.784,\n", - " 0.69389,\n", - " 0.15829,\n", - " 0.83598,\n", - " 0.84543]" + "[0.82551,\n", + " 0.86061,\n", + " 0.86736,\n", + " 0.80628,\n", + " 0.85116,\n", + " 0.83631,\n", + " 0.54877,\n", + " 0.75937,\n", + " 0.79447,\n", + " 0.77827,\n", + " 0.85218,\n", + " 0.84846,\n", + " 0.61323,\n", + " 0.8245,\n", + " 0.31792]" ] }, "execution_count": 13, @@ -506,7 +530,7 @@ { "data": { "text/plain": [ - "0.8974" + "0.86736" ] }, "execution_count": 14, @@ -527,9 +551,9 @@ "data": { "text/plain": [ "{'unit_1': 128,\n", - " 'unit_2': 41,\n", - " 'lr': 0.004371872304807245,\n", - " 'epochs': 24,\n", + " 'unit_2': 81,\n", + " 'lr': 0.006387142993161844,\n", + " 'epochs': 14,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 11}" ] @@ -551,7 +575,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -610,34 +634,34 @@ "5 trials detected for ('unit_1', 'unit_2', 'lr', 'epochs', 'batch_size')\n", "\n", "***** (1/5) *****\n", - "Search({'unit_1': 128, 'unit_2': 52, 'lr': 0.0005565835702925923, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 97, 'lr': 0.0005535560552210636, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00055: early stopping\n", - "SCORE: 0.93333 at epoch 45\n", + "Epoch 00042: early stopping\n", + "SCORE: 0.92667 at epoch 36\n", "\n", "***** (2/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 65, 'lr': 0.00013052239753580174, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 77, 'lr': 0.0025150330161023593, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00091: early stopping\n", - "SCORE: 0.924 at epoch 89\n", + "Epoch 00035: early stopping\n", + "SCORE: 0.942 at epoch 25\n", "\n", "***** (3/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 105, 'lr': 0.0023668178974410024, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 37, 'lr': 0.0005324197618194066, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00043: early stopping\n", - "SCORE: 0.93967 at epoch 33\n", + "Epoch 00037: early stopping\n", + "SCORE: 0.93733 at epoch 27\n", "\n", "***** (4/5) *****\n", - "Search({'unit_1': 128, 'unit_2': 37, 'lr': 0.015329273569045341, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 114, 'lr': 0.0005304760422961851, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00023: early stopping\n", - "SCORE: 0.94967 at epoch 13\n", + "Epoch 00042: early stopping\n", + "SCORE: 0.93533 at epoch 40\n", "\n", "***** (5/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 47, 'lr': 0.0681537581148546, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 125, 'lr': 0.010310596407937588, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00032: early stopping\n", - "SCORE: 0.90233 at epoch 24\n", + "Epoch 00022: early stopping\n", + "SCORE: 0.951 at epoch 18\n", "\n", "##################\n", "### Fold 002 ###\n", @@ -646,32 +670,34 @@ "5 trials detected for ('unit_1', 'unit_2', 'lr', 'epochs', 'batch_size')\n", "\n", "***** (1/5) *****\n", - "Search({'unit_1': 128, 'unit_2': 52, 'lr': 0.0005565835702925923, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 97, 'lr': 0.0005535560552210636, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00042: early stopping\n", - "SCORE: 0.94333 at epoch 42\n", + "Epoch 00065: early stopping\n", + "SCORE: 0.93767 at epoch 64\n", "\n", "***** (2/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 65, 'lr': 0.00013052239753580174, 'epochs': 100, 'batch_size': 512})\n", - "SCORE: 0.92267 at epoch 97\n", + "Search({'unit_1': 128, 'unit_2': 77, 'lr': 0.0025150330161023593, 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00040: early stopping\n", + "SCORE: 0.95367 at epoch 30\n", "\n", "***** (3/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 105, 'lr': 0.0023668178974410024, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 37, 'lr': 0.0005324197618194066, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00037: early stopping\n", - "SCORE: 0.947 at epoch 27\n", + "Epoch 00052: early stopping\n", + "SCORE: 0.94333 at epoch 42\n", "\n", "***** (4/5) *****\n", - "Search({'unit_1': 128, 'unit_2': 37, 'lr': 0.015329273569045341, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 114, 'lr': 0.0005304760422961851, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00036: early stopping\n", - "SCORE: 0.952 at epoch 30\n", + "Epoch 00058: early stopping\n", + "SCORE: 0.93867 at epoch 57\n", "\n", "***** (5/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 47, 'lr': 0.0681537581148546, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 125, 'lr': 0.010310596407937588, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00021: early stopping\n", - "SCORE: 0.90167 at epoch 13\n", + "Epoch 00022: early stopping\n", + "SCORE: 0.95533 at epoch 12\n", "\n", "##################\n", "### Fold 003 ###\n", @@ -680,33 +706,45 @@ "5 trials detected for ('unit_1', 'unit_2', 'lr', 'epochs', 'batch_size')\n", "\n", "***** (1/5) *****\n", - "Search({'unit_1': 128, 'unit_2': 52, 'lr': 0.0005565835702925923, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 97, 'lr': 0.0005535560552210636, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00056: early stopping\n", - "SCORE: 0.94398 at epoch 52\n", + "Epoch 00059: early stopping\n", + "SCORE: 0.93765 at epoch 59\n", "\n", "***** (2/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 65, 'lr': 0.00013052239753580174, 'epochs': 100, 'batch_size': 512})\n", - "SCORE: 0.93164 at epoch 100\n", + "Search({'unit_1': 128, 'unit_2': 77, 'lr': 0.0025150330161023593, 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00038: early stopping\n", + "SCORE: 0.95365 at epoch 28\n", "\n", "***** (3/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 105, 'lr': 0.0023668178974410024, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 37, 'lr': 0.0005324197618194066, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00038: early stopping\n", - "SCORE: 0.94498 at epoch 38\n", + "Epoch 00049: early stopping\n", + "SCORE: 0.94165 at epoch 46\n", "\n", "***** (4/5) *****\n", - "Search({'unit_1': 128, 'unit_2': 37, 'lr': 0.015329273569045341, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 64, 'unit_2': 114, 'lr': 0.0005304760422961851, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00036: early stopping\n", - "SCORE: 0.95065 at epoch 28\n", + "Epoch 00063: early stopping\n", + "SCORE: 0.94532 at epoch 53\n", "\n", "***** (5/5) *****\n", - "Search({'unit_1': 64, 'unit_2': 47, 'lr': 0.0681537581148546, 'epochs': 100, 'batch_size': 512})\n", + "Search({'unit_1': 128, 'unit_2': 125, 'lr': 0.010310596407937588, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00029: early stopping\n", - "SCORE: 0.89163 at epoch 19\n" + "Epoch 00028: early stopping\n", + "SCORE: 0.95499 at epoch 24\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -733,9 +771,9 @@ { "data": { "text/plain": [ - "{'fold 1': [0.93333, 0.924, 0.93967, 0.94967, 0.90233],\n", - " 'fold 2': [0.94333, 0.92267, 0.947, 0.952, 0.90167],\n", - " 'fold 3': [0.94398, 0.93164, 0.94498, 0.95065, 0.89163]}" + "{'fold 1': [0.92667, 0.942, 0.93733, 0.93533, 0.951],\n", + " 'fold 2': [0.93767, 0.95367, 0.94333, 0.93867, 0.95533],\n", + " 'fold 3': [0.93765, 0.95365, 0.94165, 0.94532, 0.95499]}" ] }, "execution_count": 19, @@ -755,7 +793,7 @@ { "data": { "text/plain": [ - "{'fold 1': 0.94967, 'fold 2': 0.952, 'fold 3': 0.95065}" + "{'fold 1': 0.951, 'fold 2': 0.95533, 'fold 3': 0.95499}" ] }, "execution_count": 20, @@ -776,21 +814,21 @@ "data": { "text/plain": [ "{'fold 1': {'unit_1': 128,\n", - " 'unit_2': 37,\n", - " 'lr': 0.015329273569045341,\n", - " 'epochs': 13,\n", + " 'unit_2': 125,\n", + " 'lr': 0.010310596407937588,\n", + " 'epochs': 18,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 12},\n", " 'fold 2': {'unit_1': 128,\n", - " 'unit_2': 37,\n", - " 'lr': 0.015329273569045341,\n", - " 'epochs': 30,\n", + " 'unit_2': 125,\n", + " 'lr': 0.010310596407937588,\n", + " 'epochs': 12,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 12},\n", " 'fold 3': {'unit_1': 128,\n", - " 'unit_2': 37,\n", - " 'lr': 0.015329273569045341,\n", - " 'epochs': 28,\n", + " 'unit_2': 125,\n", + " 'lr': 0.010310596407937588,\n", + " 'epochs': 24,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 12}}" ] @@ -812,9 +850,9 @@ { "data": { "text/plain": [ - "{'fold 1': ,\n", - " 'fold 2': ,\n", - " 'fold 3': }" + "{'fold 1': ,\n", + " 'fold 2': ,\n", + " 'fold 3': }" ] }, "execution_count": 22, @@ -834,7 +872,7 @@ { "data": { "text/plain": [ - "0.95077" + "0.95377" ] }, "execution_count": 23, @@ -855,21 +893,21 @@ "data": { "text/plain": [ "[{'unit_1': 128,\n", - " 'unit_2': 37,\n", - " 'lr': 0.015329273569045341,\n", - " 'epochs': 13,\n", + " 'unit_2': 125,\n", + " 'lr': 0.010310596407937588,\n", + " 'epochs': 18,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 12},\n", " {'unit_1': 128,\n", - " 'unit_2': 37,\n", - " 'lr': 0.015329273569045341,\n", - " 'epochs': 30,\n", + " 'unit_2': 125,\n", + " 'lr': 0.010310596407937588,\n", + " 'epochs': 12,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 12},\n", " {'unit_1': 128,\n", - " 'unit_2': 37,\n", - " 'lr': 0.015329273569045341,\n", - " 'epochs': 28,\n", + " 'unit_2': 125,\n", + " 'lr': 0.010310596407937588,\n", + " 'epochs': 24,\n", " 'batch_size': 512,\n", " 'steps_per_epoch': 12}]" ] @@ -900,7 +938,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/notebooks/Multi-Input Multi-Output Search.ipynb b/notebooks/Multi-Input Multi-Output Search.ipynb index 441a04c..4641deb 100644 --- a/notebooks/Multi-Input Multi-Output Search.ipynb +++ b/notebooks/Multi-Input Multi-Output Search.ipynb @@ -231,6 +231,16 @@ "Epoch 00035: early stopping\n", "SCORE: 0.26808 at epoch 32\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -313,7 +323,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -448,197 +458,207 @@ "32 trials detected for ('unit_1', 'unit_2', 'activ', 'lr', 'epochs', 'batch_size')\n", "\n", "***** (1/32) *****\n", - "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", - "Restoring model weights from the end of the best epoch.\n", - "Epoch 00008: early stopping\n", - "SCORE: 0.1787 at epoch 3\n", - "\n", - "***** (2/32) *****\n", - "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", - "Restoring model weights from the end of the best epoch.\n", - "Epoch 00008: early stopping\n", - "SCORE: 0.17906 at epoch 3\n", - "\n", - "***** (3/32) *****\n", - "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", - "Restoring model weights from the end of the best epoch.\n", - "Epoch 00014: early stopping\n", - "SCORE: 0.18686 at epoch 9\n", - "\n", - "***** (4/32) *****\n", - "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", - "Restoring model weights from the end of the best epoch.\n", - "Epoch 00016: early stopping\n", - "SCORE: 0.19279 at epoch 12\n", - "\n", - "***** (5/32) *****\n", "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'relu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00008: early stopping\n", "SCORE: 0.18378 at epoch 3\n", "\n", - "***** (6/32) *****\n", + "***** (2/32) *****\n", "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'relu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00011: early stopping\n", "SCORE: 0.18168 at epoch 6\n", "\n", - "***** (7/32) *****\n", + "***** (3/32) *****\n", "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'relu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00017: early stopping\n", "SCORE: 0.16768 at epoch 12\n", "\n", - "***** (8/32) *****\n", + "***** (4/32) *****\n", "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'relu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00025: early stopping\n", "SCORE: 0.17568 at epoch 20\n", "\n", - "***** (9/32) *****\n", - "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", + "***** (5/32) *****\n", + "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00008: early stopping\n", - "SCORE: 0.17126 at epoch 3\n", + "SCORE: 0.1787 at epoch 3\n", "\n", - "***** (10/32) *****\n", - "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", + "***** (6/32) *****\n", + "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00009: early stopping\n", - "SCORE: 0.17232 at epoch 4\n", + "Epoch 00008: early stopping\n", + "SCORE: 0.17906 at epoch 3\n", "\n", - "***** (11/32) *****\n", - "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", + "***** (7/32) *****\n", + "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00014: early stopping\n", - "SCORE: 0.19072 at epoch 10\n", + "SCORE: 0.18686 at epoch 9\n", "\n", - "***** (12/32) *****\n", - "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", + "***** (8/32) *****\n", + "Search({'unit_1': 128, 'unit_2': 64, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00018: early stopping\n", - "SCORE: 0.20002 at epoch 14\n", + "Epoch 00016: early stopping\n", + "SCORE: 0.19279 at epoch 12\n", "\n", - "***** (13/32) *****\n", + "***** (9/32) *****\n", "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'relu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00011: early stopping\n", "SCORE: 0.18653 at epoch 6\n", "\n", - "***** (14/32) *****\n", + "***** (10/32) *****\n", "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'relu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00021: early stopping\n", "SCORE: 0.18344 at epoch 16\n", "\n", - "***** (15/32) *****\n", + "***** (11/32) *****\n", "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'relu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00017: early stopping\n", "SCORE: 0.18323 at epoch 12\n", "\n", - "***** (16/32) *****\n", + "***** (12/32) *****\n", "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'relu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00027: early stopping\n", "SCORE: 0.17574 at epoch 22\n", "\n", - "***** (17/32) *****\n", - "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", + "***** (13/32) *****\n", + "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00008: early stopping\n", - "SCORE: 0.1742 at epoch 3\n", + "SCORE: 0.17126 at epoch 3\n", "\n", - "***** (18/32) *****\n", - "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", + "***** (14/32) *****\n", + "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00010: early stopping\n", - "SCORE: 0.1817 at epoch 5\n", + "Epoch 00009: early stopping\n", + "SCORE: 0.17232 at epoch 4\n", "\n", - "***** (19/32) *****\n", - "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", + "***** (15/32) *****\n", + "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00014: early stopping\n", - "SCORE: 0.19459 at epoch 11\n", + "SCORE: 0.19072 at epoch 10\n", "\n", - "***** (20/32) *****\n", - "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", + "***** (16/32) *****\n", + "Search({'unit_1': 128, 'unit_2': 32, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00019: early stopping\n", - "SCORE: 0.19545 at epoch 15\n", + "Epoch 00018: early stopping\n", + "SCORE: 0.20002 at epoch 14\n", "\n", - "***** (21/32) *****\n", + "***** (17/32) *****\n", "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'relu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00010: early stopping\n", "SCORE: 0.18135 at epoch 5\n", "\n", - "***** (22/32) *****\n", + "***** (18/32) *****\n", "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'relu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00010: early stopping\n", "SCORE: 0.17467 at epoch 5\n", "\n", - "***** (23/32) *****\n", + "***** (19/32) *****\n", "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'relu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00018: early stopping\n", "SCORE: 0.18343 at epoch 13\n", "\n", - "***** (24/32) *****\n", + "***** (20/32) *****\n", "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'relu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00025: early stopping\n", "SCORE: 0.1851 at epoch 22\n", "\n", - "***** (25/32) *****\n", - "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", + "***** (21/32) *****\n", + "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00008: early stopping\n", - "SCORE: 0.17806 at epoch 3\n", + "SCORE: 0.1742 at epoch 3\n", "\n", - "***** (26/32) *****\n", - "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", + "***** (22/32) *****\n", + "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00010: early stopping\n", - "SCORE: 0.1804 at epoch 5\n", + "SCORE: 0.1817 at epoch 5\n", "\n", - "***** (27/32) *****\n", - "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", + "***** (23/32) *****\n", + "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00016: early stopping\n", - "SCORE: 0.20165 at epoch 11\n", + "Epoch 00014: early stopping\n", + "SCORE: 0.19459 at epoch 11\n", "\n", - "***** (28/32) *****\n", - "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", + "***** (24/32) *****\n", + "Search({'unit_1': 64, 'unit_2': 64, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", - "Epoch 00021: early stopping\n", - "SCORE: 0.20478 at epoch 16\n", + "Epoch 00019: early stopping\n", + "SCORE: 0.19545 at epoch 15\n", "\n", - "***** (29/32) *****\n", + "***** (25/32) *****\n", "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'relu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00010: early stopping\n", "SCORE: 0.1916 at epoch 5\n", "\n", - "***** (30/32) *****\n", + "***** (26/32) *****\n", "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'relu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00012: early stopping\n", "SCORE: 0.19075 at epoch 9\n", "\n", - "***** (31/32) *****\n", + "***** (27/32) *****\n", "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'relu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00018: early stopping\n", "SCORE: 0.19666 at epoch 13\n", "\n", - "***** (32/32) *****\n", + "***** (28/32) *****\n", "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'relu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", "Restoring model weights from the end of the best epoch.\n", "Epoch 00027: early stopping\n", - "SCORE: 0.19022 at epoch 22\n" + "SCORE: 0.19022 at epoch 22\n", + "\n", + "***** (29/32) *****\n", + "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 256})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00008: early stopping\n", + "SCORE: 0.17806 at epoch 3\n", + "\n", + "***** (30/32) *****\n", + "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'elu', 'lr': 0.01, 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00010: early stopping\n", + "SCORE: 0.1804 at epoch 5\n", + "\n", + "***** (31/32) *****\n", + "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 256})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00016: early stopping\n", + "SCORE: 0.20165 at epoch 11\n", + "\n", + "***** (32/32) *****\n", + "Search({'unit_1': 64, 'unit_2': 32, 'activ': 'elu', 'lr': 0.001, 'epochs': 100, 'batch_size': 512})\n", + "Restoring model weights from the end of the best epoch.\n", + "Epoch 00021: early stopping\n", + "SCORE: 0.20478 at epoch 16\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -659,7 +679,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.1787, 0.17906, 0.18686, 0.19279, 0.18378, 0.18168, 0.16768, 0.17568, 0.17126, 0.17232, 0.19072, 0.20002, 0.18653, 0.18344, 0.18323, 0.17574, 0.1742, 0.1817, 0.19459, 0.19545, 0.18135, 0.17467, 0.18343, 0.1851, 0.17806, 0.1804, 0.20165, 0.20478, 0.1916, 0.19075, 0.19666, 0.19022]\n" + "[0.18378, 0.18168, 0.16768, 0.17568, 0.1787, 0.17906, 0.18686, 0.19279, 0.18653, 0.18344, 0.18323, 0.17574, 0.17126, 0.17232, 0.19072, 0.20002, 0.18135, 0.17467, 0.18343, 0.1851, 0.1742, 0.1817, 0.19459, 0.19545, 0.1916, 0.19075, 0.19666, 0.19022, 0.17806, 0.1804, 0.20165, 0.20478]\n" ] } ], @@ -721,7 +741,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 19, @@ -750,7 +770,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.4" } }, "nbformat": 4,