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This commit is contained in:
parent
fb2e70e1da
commit
8e4927d3ea
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 54,
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"id": "f902472d",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 55,
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"id": "2324a8dd",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 56,
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"id": "e4ba4b52",
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"metadata": {},
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"outputs": [
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@ -144,7 +144,7 @@
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"9 2017.005479 20170103 Sinn Féin warns Stormont may collapse over 'ca..."
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]
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},
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"execution_count": 30,
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"execution_count": 56,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 57,
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"id": "d4a64cb8",
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"metadata": {},
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"outputs": [
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@ -264,7 +264,7 @@
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"9 2012.791781 20121016 UK investigation into Icelandic bank fraud aba..."
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]
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},
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"execution_count": 31,
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"execution_count": 57,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -275,7 +275,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 58,
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"id": "1221baee",
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"metadata": {},
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"outputs": [
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@ -384,7 +384,7 @@
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"9 2005.569863 20050728 IRA must hand over criminal assets - McDowell"
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]
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},
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"execution_count": 32,
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"execution_count": 58,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -395,7 +395,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 59,
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"id": "58cb7b89",
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"metadata": {},
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"outputs": [
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@ -482,7 +482,7 @@
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"9 news"
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]
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},
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"execution_count": 33,
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"execution_count": 59,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -493,7 +493,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 60,
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"id": "cfb113b6",
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"metadata": {},
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"outputs": [],
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@ -506,7 +506,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 61,
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"id": "046f00be",
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"metadata": {},
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"outputs": [
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@ -516,7 +516,7 @@
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"'Sudan claims it is disarming militias'"
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]
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},
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"execution_count": 35,
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"execution_count": 61,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 62,
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"id": "9d36394d",
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"metadata": {},
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"outputs": [
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@ -548,7 +548,7 @@
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"Name: 2, Length: 1186898, dtype: object"
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]
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},
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"execution_count": 36,
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"execution_count": 62,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 63,
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"id": "58d6e666",
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"metadata": {},
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"outputs": [
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" 'removed'], dtype=object)"
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]
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},
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"execution_count": 37,
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"execution_count": 63,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 64,
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"id": "86d6f712",
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"metadata": {},
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"outputs": [],
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@ -594,7 +594,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 65,
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"id": "4491cae8",
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"metadata": {},
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"outputs": [
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"3 3"
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]
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},
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"execution_count": 39,
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"execution_count": 65,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 66,
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"id": "6eccbc39",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 67,
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"id": "e09e6a3f",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 68,
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"id": "f0e4b5fc",
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"metadata": {},
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"outputs": [
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"pandas.core.frame.DataFrame"
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]
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},
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"execution_count": 42,
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"execution_count": 68,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 69,
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"id": "7662ca93",
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"metadata": {},
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"outputs": [
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"pandas.core.series.Series"
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]
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},
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"execution_count": 43,
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"execution_count": 69,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"execution_count": 70,
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"id": "a1838cd6",
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"metadata": {},
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"outputs": [
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" ...]"
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]
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},
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"execution_count": 44,
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"execution_count": 70,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"execution_count": 71,
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"id": "3eedae48",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"execution_count": 72,
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"id": "adc7bcd0",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 73,
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"id": "2b9ce936",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"execution_count": 74,
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"id": "cef5f0c2",
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"metadata": {},
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"outputs": [
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" '| headline: UK investigation into Icelandic bank fraud abandoned']"
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]
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},
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"execution_count": 48,
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"execution_count": 74,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"execution_count": 75,
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"id": "062f0bd1",
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"metadata": {},
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"outputs": [
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" \"| headline: Those who can't\"]"
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]
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},
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"execution_count": 49,
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"execution_count": 75,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"execution_count": 76,
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"id": "f20d5d1d",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"execution_count": 77,
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"id": "4c68c041",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"execution_count": 78,
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"id": "9da03434",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"execution_count": 79,
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"id": "f8d5471d",
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "948f6088",
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"id": "37353752",
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"metadata": {},
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"outputs": [],
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"source": []
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@ -1,33 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "977d76ed",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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170
keras_class.ipynb
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170
keras_class.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9e3e0aed",
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"metadata": {},
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"outputs": [],
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"source": [
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"from keras.preprocessing.image import ImageDataGenerator\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Conv2D, MaxPooling2D\n",
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"from keras.layers import Activation, Dropout, Flatten, Dense\n",
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"from keras import backend as K\n",
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" \n",
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"img_width, img_height = 224, 224"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "976c0dc7",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data_dir = 'v_data/train'\n",
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"validation_data_dir = 'v_data/test'\n",
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"nb_train_samples =400\n",
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"nb_validation_samples = 100\n",
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"epochs = 10\n",
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"batch_size = 16"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9bf78481",
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"metadata": {},
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"outputs": [],
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"source": [
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"# check format\n",
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"\n",
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"\n",
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"if K.image_data_format() == 'channels_first':\n",
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" input_shape = (3, img_width, img_height)\n",
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"else:\n",
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" input_shape = (img_width, img_height, 3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a0663374",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = Sequential()\n",
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"model.add(Conv2D(32, (2, 2), input_shape=input_shape))\n",
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"model.add(Activation('relu'))\n",
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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" \n",
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"model.add(Conv2D(32, (2, 2)))\n",
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"model.add(Activation('relu'))\n",
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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" \n",
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"model.add(Conv2D(64, (2, 2)))\n",
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"model.add(Activation('relu'))\n",
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"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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" \n",
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"model.add(Flatten())\n",
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"model.add(Dense(64))\n",
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"model.add(Activation('relu'))\n",
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"model.add(Dropout(0.5))\n",
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"model.add(Dense(1))\n",
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"model.add(Activation('sigmoid'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "6fadd7e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"model.compile(loss='binary_crossentropy',\n",
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" optimizer='rmsprop',\n",
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" metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "0bb1a7ba",
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"metadata": {},
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[WinError 3] The system cannot find the path specified: 'v_data/train'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 9>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m train_datagen \u001b[38;5;241m=\u001b[39m ImageDataGenerator(\n\u001b[0;32m 2\u001b[0m rescale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255\u001b[39m,\n\u001b[0;32m 3\u001b[0m shear_range\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m,\n\u001b[0;32m 4\u001b[0m zoom_range\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m,\n\u001b[0;32m 5\u001b[0m horizontal_flip\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 7\u001b[0m test_datagen \u001b[38;5;241m=\u001b[39m ImageDataGenerator(rescale\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m train_generator \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_datagen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflow_from_directory\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_data_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mimg_width\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimg_height\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mclass_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbinary\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 15\u001b[0m validation_generator \u001b[38;5;241m=\u001b[39m test_datagen\u001b[38;5;241m.\u001b[39mflow_from_directory(\n\u001b[0;32m 16\u001b[0m validation_data_dir,\n\u001b[0;32m 17\u001b[0m target_size\u001b[38;5;241m=\u001b[39m(img_width, img_height),\n\u001b[0;32m 18\u001b[0m batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[0;32m 19\u001b[0m class_mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 21\u001b[0m model\u001b[38;5;241m.\u001b[39mfit_generator(\n\u001b[0;32m 22\u001b[0m train_generator,\n\u001b[0;32m 23\u001b[0m steps_per_epoch\u001b[38;5;241m=\u001b[39mnb_train_samples \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m batch_size,\n\u001b[0;32m 24\u001b[0m epochs\u001b[38;5;241m=\u001b[39mepochs,\n\u001b[0;32m 25\u001b[0m validation_data\u001b[38;5;241m=\u001b[39mvalidation_generator,\n\u001b[0;32m 26\u001b[0m validation_steps\u001b[38;5;241m=\u001b[39mnb_validation_samples \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m batch_size)\n",
|
||||
"File \u001b[1;32m~\\.conda\\envs\\py\\lib\\site-packages\\keras\\preprocessing\\image.py:1469\u001b[0m, in \u001b[0;36mImageDataGenerator.flow_from_directory\u001b[1;34m(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, keep_aspect_ratio)\u001b[0m\n\u001b[0;32m 1386\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mflow_from_directory\u001b[39m(\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 1387\u001b[0m directory,\n\u001b[0;32m 1388\u001b[0m target_size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m256\u001b[39m, \u001b[38;5;241m256\u001b[39m),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1400\u001b[0m interpolation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnearest\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 1401\u001b[0m keep_aspect_ratio\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m 1402\u001b[0m \u001b[38;5;124;03m\"\"\"Takes the path to a directory & generates batches of augmented data.\u001b[39;00m\n\u001b[0;32m 1403\u001b[0m \n\u001b[0;32m 1404\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1467\u001b[0m \u001b[38;5;124;03m and `y` is a numpy array of corresponding labels.\u001b[39;00m\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1469\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDirectoryIterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1470\u001b[0m \u001b[43m \u001b[49m\u001b[43mdirectory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1471\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1472\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1473\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolor_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1474\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_aspect_ratio\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_aspect_ratio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1475\u001b[0m \u001b[43m \u001b[49m\u001b[43mclasses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclasses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1476\u001b[0m \u001b[43m \u001b[49m\u001b[43mclass_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1477\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1478\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1479\u001b[0m \u001b[43m \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1480\u001b[0m \u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1481\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_to_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_to_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1482\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_prefix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_prefix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1483\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1484\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_links\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_links\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1485\u001b[0m \u001b[43m \u001b[49m\u001b[43msubset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1486\u001b[0m \u001b[43m \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1487\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32m~\\.conda\\envs\\py\\lib\\site-packages\\keras\\preprocessing\\image.py:507\u001b[0m, in \u001b[0;36mDirectoryIterator.__init__\u001b[1;34m(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, keep_aspect_ratio, dtype)\u001b[0m\n\u001b[0;32m 505\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m classes:\n\u001b[0;32m 506\u001b[0m classes \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m--> 507\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m subdir \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdirectory\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[0;32m 508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(directory, subdir)):\n\u001b[0;32m 509\u001b[0m classes\u001b[38;5;241m.\u001b[39mappend(subdir)\n",
|
||||
"\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: 'v_data/train'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"train_datagen = ImageDataGenerator(\n",
|
||||
" rescale=1. / 255,\n",
|
||||
" shear_range=0.2,\n",
|
||||
" zoom_range=0.2,\n",
|
||||
" horizontal_flip=True)\n",
|
||||
" \n",
|
||||
"test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
|
||||
" \n",
|
||||
"train_generator = train_datagen.flow_from_directory(\n",
|
||||
" train_data_dir,\n",
|
||||
" target_size=(img_width, img_height),\n",
|
||||
" batch_size=batch_size,\n",
|
||||
" class_mode='binary')\n",
|
||||
" \n",
|
||||
"validation_generator = test_datagen.flow_from_directory(\n",
|
||||
" validation_data_dir,\n",
|
||||
" target_size=(img_width, img_height),\n",
|
||||
" batch_size=batch_size,\n",
|
||||
" class_mode='binary')\n",
|
||||
" \n",
|
||||
"model.fit_generator(\n",
|
||||
" train_generator,\n",
|
||||
" steps_per_epoch=nb_train_samples // batch_size,\n",
|
||||
" epochs=epochs,\n",
|
||||
" validation_data=validation_generator,\n",
|
||||
" validation_steps=nb_validation_samples // batch_size)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "623ec03f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "py",
|
||||
"language": "python",
|
||||
"name": "py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user