Wykład 4 - poprawka kodu
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@ -2480,7 +2480,7 @@
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"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
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"y_train = keras.utils.to_categorical(y_train, num_classes)\n",
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"y_test = keras.utils.to_categorical(y_test, num_classes)\n",
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"y_test = keras.utils.to_categorical(y_test, num_classes)\n",
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"\n",
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"\n",
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"model3 = Sequential()\n",
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"model3 = keras.Sequential()\n",
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"model3.add(Dense(2500, activation='tanh', input_shape=(784,)))\n",
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"model3.add(Dense(2500, activation='tanh', input_shape=(784,)))\n",
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"model3.add(Dense(2000, activation='tanh'))\n",
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"model3.add(Dense(2000, activation='tanh'))\n",
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"model3.add(Dense(1500, activation='tanh'))\n",
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"model3.add(Dense(1500, activation='tanh'))\n",
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@ -2868,7 +2868,7 @@
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}
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}
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],
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],
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"source": [
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"source": [
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"model5 = Sequential()\n",
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"model5 = keras.Sequential()\n",
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"model5.add(Dense(4, activation='tanh', input_shape=(4,)))\n",
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"model5.add(Dense(4, activation='tanh', input_shape=(4,)))\n",
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"model5.add(Dense(4, activation='tanh'))\n",
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"model5.add(Dense(4, activation='tanh'))\n",
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"model5.add(Dense(8, activation='relu'))\n",
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"model5.add(Dense(8, activation='relu'))\n",
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@ -3058,7 +3058,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.8.3"
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},
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},
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"livereveal": {
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"livereveal": {
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"start_slideshow_at": "selected",
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"start_slideshow_at": "selected",
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