compress with a maxpool
"base_uri": "https://localhost:8080/"
},
"id": "POVbu2vrnDmS",
- "outputId": "eb19d53a-0a90-4165-a705-6dbb0f0f1983"
+ "outputId": "1e4b02a0-e7f9-462f-8d10-dce743dd2b6e"
},
"outputs": [],
"source": [
"base_uri": "https://localhost:8080/"
},
"id": "GfIt6ECGnDmS",
- "outputId": "7266ccc0-378a-400c-e80b-7228b005adbc"
+ "outputId": "b39a6197-056b-4c95-c47a-2ba60d21e9f7"
},
"outputs": [],
"source": [
"height": 313
},
"id": "A0u1bRevJLRc",
- "outputId": "cbc717c0-16cf-43f8-b6d1-924d631c5d82"
+ "outputId": "eb0a120b-bb1e-4da2-da54-f123b453526f"
},
"outputs": [],
"source": [
"\n",
"x = tf.keras.layers.Conv3D(\n",
" filters=1,\n",
- " kernel_size=(z_size, 1,1),\n",
- " padding=\"valid\",\n",
+ " kernel_size=1,\n",
" activation=\"sigmoid\",\n",
" kernel_initializer=\"he_normal\",\n",
")(x)\n",
"\n",
+ "x = tf.keras.layers.MaxPool3D(\n",
+ " pool_size=(z_size, 1, 1)\n",
+ ")(x)\n",
+ "\n",
"model = tf.keras.Model(inputs=inputs, outputs=x)"
]
},
"base_uri": "https://localhost:8080/"
},
"id": "bcnWXRGmJLRf",
- "outputId": "2a93bc34-1a62-4934-87fc-3072d1ef24e8"
+ "outputId": "73c960f9-77b6-43cf-ad72-f8faa675f9aa"
},
"outputs": [],
"source": [
"height": 313
},
"id": "EHYE0T1VJLRf",
- "outputId": "20885457-498a-4394-cee6-90d1c3cdf503"
+ "outputId": "892b1859-ea69-42d4-8391-2697d2a977bc"
},
"outputs": [],
"source": [
"base_uri": "https://localhost:8080/"
},
"id": "0TNPQy5AJLRg",
- "outputId": "fced2ba6-0a93-4dbf-8c35-607c4cb3f0c9"
+ "outputId": "f856ffea-c407-43e9-e6e9-182a9bd71d4a"
},
"outputs": [],
"source": [
