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complete Lab24

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Rokas Puzonas 2024-03-21 00:01:35 +02:00
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"The tensorboard extension is already loaded. To reload it, use:\n",
" %reload_ext tensorboard\n",
"WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe3e8248550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
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" [0.01205833]]\n"
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"Reusing TensorBoard on port 6008 (pid 911540), started 0:22:54 ago. (Use '!kill 911540' to kill it.)"
]
},
"metadata": {},
"output_type": "display_data"
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" <iframe id=\"tensorboard-frame-e941beefef1b33a0\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
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" (function() {\n",
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" const url = new URL(\"/\", window.location);\n",
" const port = 6008;\n",
" if (port) {\n",
" url.port = port;\n",
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"source": [
"%load_ext tensorboard\n",
"\n",
"import tensorflow as tf\n",
"import numpy as np \n",
"import datetime, os\n",
"\n",
"tf.config.experimental.set_visible_devices([], \"GPU\") \n",
"\n",
"X = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
"y = np.array([[0],[1],[1],[0]])\n",
" \n",
"model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Dense(8, activation='relu', name='layers_dense_1'),\n",
" tf.keras.layers.Dense(8, activation='relu', name='layers_dense_2'),\n",
" tf.keras.layers.Dense(1, activation='sigmoid', name='layers_dense_3')\n",
"])\n",
" \n",
"loss_fn = tf.keras.losses.binary_crossentropy\n",
"\n",
"simple = True\n",
"\n",
"if simple == True:\n",
" model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
" \n",
" logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
" tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
" model.fit(X, y, batch_size=4, epochs=1000, verbose=0, callbacks = [tensorboard_callback])\n",
"else:\n",
" for i in range(100):\n",
" with tf.GradientTape() as tape:\n",
" # Forward pass.\n",
" predictions = model(X)\n",
" # Compute the loss value for this batch.\n",
" loss_value = loss_fn(y, predictions)\n",
"\n",
" # Get gradients of loss wrt the weights.\n",
" gradients = tape.gradient(loss_value, model.trainable_weights)\n",
" # Update the weights of the model.\n",
" optimizer.apply_gradients(zip(gradients, model.trainable_weights))\n",
"\n",
"print(model.predict(X))\n",
"\n",
"%tensorboard --logdir logs"
]
}
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Lab2/examples/datasets/iris_test.csv Normal file
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6.4,2.9,4.3,1.3,1
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2 6.9 3.1 5.4 2.1 2
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7 5.5 4.2 1.4 0.2 0
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Lab2/examples/datasets/iris_train.csv Normal file
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5,3.3,1.4,0.2,0
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5,3.2,1.2,0.2,0
7,3.2,4.7,1.4,1
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7.4,2.8,6.1,1.9,2
5.8,2.7,5.1,1.9,2
6.2,3.4,5.4,2.3,2
5,2,3.5,1,1
5.6,2.5,3.9,1.1,1
6.7,3.1,5.6,2.4,2
6.3,2.5,5,1.9,2
6.4,3.1,5.5,1.8,2
6.2,2.2,4.5,1.5,1
7.3,2.9,6.3,1.8,2
4.4,3,1.3,0.2,0
7.2,3.6,6.1,2.5,2
6.5,3,5.5,1.8,2
5,3.4,1.5,0.2,0
4.7,3.2,1.3,0.2,0
6.6,2.9,4.6,1.3,1
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6.4,2.7,5.3,1.9,2
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