DjangoBB LoFi version

PNicolai

Окт. 13, 2021 10:49:22

{
“nbformat”: 4,
“nbformat_minor”: 0,
“metadata”: {
“colab”: {
“name”: “Копия блокнота \”EX_rock_paper_scissors.ipynb\“”,
“provenance”: ,
“collapsed_sections”:
},
“kernelspec”: {
“name”: “python3”,
“display_name”: “Python 3”
}
},
“cells”: [
{
“cell_type”: “markdown”,
“metadata”: {
“id”: “PzykvpdaMv7A”
},
“source”: [
“##**Rock, Paper, and Scissors** \n”,
“# The proposed assignment is a multiclass image recognition task. You should create, compile, and train your CNN model using the \”Rock, Paper, and Scissors\“ open dataset.\n”,
“# Note: You need to return the .ipynb file after running your codes along with the results\n”,
“#The accepted result: No variance and no bias - Training accuracy >= 98% \n”,
“#The maximum gap between the validation and training accuracies must be 3%.\n”,
“# For getting more information, please watch the \”CNN_assignment_guide\“ video.\n”,
“# Moreover, you would need to study the related lesson if you have some misunderstanding here.\n”,
“# The submission Deadline: 13.10.2021 at 12:45 pm.”
]
},
{
“cell_type”: “code”,
“metadata”: {
“id”: “it1c0jCiNCIM”,
“colab”: {
“base_uri”: "https://localhost:8080/“
},
”outputId“: ”bf936255-b179-41a7-a939-c6a84adc3198“
},
”source“: [
”\n“,
”!wget –no-check-certificate \\\n“,
” https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip \\\n“,
” -O /tmp/rps.zip\n“,
” \n“,
”!wget –no-check-certificate \\\n“,
” https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip \\\n“,
” -O /tmp/rps-test-set.zip“
],
”execution_count“: null,
”outputs“: [
{
”output_type“: ”stream“,
”name“: ”stdout“,
”text“: [
”–2021-10-12 17:55:21– https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip\n“,
”Resolving storage.googleapis.com (storage.googleapis.com)… 64.233.189.128, 108.177.97.128, 108.177.125.128, …\n“,
”Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.189.128|:443… connected.\n“,
”HTTP request sent, awaiting response… 200 OK\n“,
”Length: 200682221 (191M) \n“,
”Saving to: ‘/tmp/rps.zip’\n“,
”\n“,
”/tmp/rps.zip 100% 191.38M 33.2MB/s in 5.8s \n“,
”\n“,
”2021-10-12 17:55:27 (33.2 MB/s) - ‘/tmp/rps.zip’ saved \n“,
”\n“,
”–2021-10-12 17:55:28– https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip\n“,
”Resolving storage.googleapis.com (storage.googleapis.com)… 108.177.125.128, 142.251.8.128, 74.125.203.128, …\n“,
”Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.125.128|:443… connected.\n“,
”HTTP request sent, awaiting response… 200 OK\n“,
”Length: 29516758 (28M) \n“,
”Saving to: ‘/tmp/rps-test-set.zip’\n“,
”\n“,
”/tmp/rps-test-set.z 100% 28.15M 51.3MB/s in 0.5s \n“,
”\n“,
”2021-10-12 17:55:29 (51.3 MB/s) - ‘/tmp/rps-test-set.zip’ saved \n“,
”\n“
]
}
]
},
{
”cell_type“: ”code“,
”metadata“: {
”id“: ”PnYP_HhYNVUK“
},
”source“: [
”import os\n“,
”import zipfile\n“,
”\n“,
”local_zip = ‘/tmp/rps.zip’\n“,
”zip_ref = zipfile.ZipFile(local_zip, ‘r’)\n“,
”zip_ref.extractall('/tmp/')\n“,
”zip_ref.close()\n“,
”\n“,
”local_zip = ‘/tmp/rps-test-set.zip’\n“,
”zip_ref = zipfile.ZipFile(local_zip, ‘r’)\n“,
”zip_ref.extractall('/tmp/')\n“,
”zip_ref.close()“
],
”execution_count“: null,
”outputs":
},
{
“cell_type”: “code”,
“metadata”: {
“id”: “MrxdR83ANgjS”,
“colab”: {
“base_uri”: "https://localhost:8080/“
},
”outputId“: ”b66e6e9d-b53d-46b2-db0d-2bf675fb4303“
},
”source“: [
”rock_dir = os.path.join('/tmp/rps/rock')\n“,
”paper_dir = os.path.join('/tmp/rps/paper')\n“,
”scissors_dir = os.path.join('/tmp/rps/scissors')\n“,
”\n“,
”print('total training rock images:', len(os.listdir(rock_dir)))\n“,
”print('total training paper images:', len(os.listdir(paper_dir)))\n“,
”print('total training scissors images:', len(os.listdir(scissors_dir)))\n“,
”\n“,
”rock_files = os.listdir(rock_dir)\n“,
”print(rock_files)\n“,
”\n“,
”paper_files = os.listdir(paper_dir)\n“,
”print(paper_files)\n“,
”\n“,
”scissors_files = os.listdir(scissors_dir)\n“,
”print(scissors_files)“
],
”execution_count“: null,
”outputs“: [
{
”output_type“: ”stream“,
”name“: ”stdout“,
”text“: [
”total training rock images: 840\n“,
”total training paper images: 840\n“,
”total training scissors images: 840\n“,
”\n“,
”\n“,
”\n“
]
}
]
},
{
”cell_type“: ”code“,
”metadata“: {
”id“: ”jp9dLel9N9DS“
},
”source“: [
”%matplotlib inline\n“,
”\n“,
”import matplotlib.pyplot as plt\n“,
”import matplotlib.image as mpimg\n“,
”\n“,
”pic_index = 2\n“,
”\n“,
”next_rock = [os.path.join(rock_dir, fname) \n“,
” for fname in rock_files]\n“,
”next_paper = [os.path.join(paper_dir, fname) \n“,
” for fname in paper_files]\n“,
”next_scissors = [os.path.join(scissors_dir, fname) \n“,
” for fname in scissors_files]\n“,
”\n“,
”for i, img_path in enumerate(next_rock+next_paper+next_scissors):\n“,
” #print(img_path)\n“,
” img = mpimg.imread(img_path)\n“,
” #plt.imshow(img)\n“,
” #plt.axis('Off')\n“,
” #plt.show()“
],
”execution_count“: null,
”outputs":
},
{
“cell_type”: “code”,
“metadata”: {
“id”: “_L8H_zg2O4cS”,
“colab”: {
“base_uri”: "https://localhost:8080/“,
”height“: 978
},
”outputId“: ”e44fb4cd-7961-4410-986f-717546c6e33c“
},
”source“: [
”# YOUR CODE HERE\n“,
”# START\n“,
”# myCallback, acc > 0.98\n“,
”import tensorflow as tf\n“,
”class myCallback(tf.keras.callbacks.Callback):\n“,
” def on_epoch_end(self, epoch, logs={}):\n“,
” if(logs.get('accuracy')>0.98):\n“,
” print(\“\\nReached 98% accuracy so cancelling training!\”)\n“,
” self.model.stop_training = True\n“,
”\n“,
”callbacks=myCallback()\n“,
”\n“,
”model= tf.keras.models.Sequential([\n“,
” tf.keras.layers.Flatten(input_shape

28, 28)),\n“,
” tf.keras.layers.Dense(512, activation=tf.nn.relu),\n“,
” tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n“,
”])\n“,
”\n“,
”model.compile(optimizer='adam',\n“,
” loss='sparse_categorical_crossentropy',\n“,
” metrics

\n“,
”\n“,
”model.fit(img, epochs=6, callbacks

\n“,
”\n“,
”\n“,
”# END“
],
”execution_count“: null,
”outputs“: [
{
”output_type“: ”stream“,
”name“: ”stdout“,
”text“: [
”Epoch 1/6\n“,
”WARNING:tensorflow:Model was constructed with shape (None, 28, 28) for input KerasTensor(type_spec=TensorSpec(shape

None, 28, 28), dtype=tf.float32, name='flatten_3_input'), name='flatten_3_input', description=\“created by layer ‘flatten_3_input’\”), but it was called on an input with incompatible shape (None, 300, 4).\n“
]
},
{
”output_type“: ”error“,
”ename“: ”ValueError“,
”evalue“: ”ignored“,
”traceback“: [
”\u001b[0;31m—————————————————————————\u001b[0m“,
”\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)“,
”\u001b[0;32m<ipython-input-8-b7ae152dd0cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b)\n\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m—> 23\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1182\u001b[0m _r=1):\n\u001b[1;32m 1183\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1185\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 884\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m–> 885\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 931\u001b[0m \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 932\u001b[0m \u001b[0minitializers\u001b[0m \u001b\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m–> 933\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 934\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 935\u001b[0m \u001b[0;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m 758\u001b[0m self._concrete_stateful_fn = (\n\u001b[1;32m 759\u001b[0m self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access\n\u001b[0;32m–> 760\u001b[0;31m *args, **kwds))\n\u001b[0m\u001b[1;32m 761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 762\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3064\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3065\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3066\u001b[0;31m \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3067\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m 3461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3462\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3463\u001b[0;31m \u001b[0mgraph_function\u001b[0m \u001b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m 3306\u001b[0m \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3307\u001b[0m \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3308\u001b[0;31m capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m 3309\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3310\u001b[0m \u001b[0mfunction_spec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_spec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)\u001b[0m\n\u001b[1;32m 1005\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1009\u001b[0m \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompile_with_xla\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m–> 668\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 669\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 992\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 993\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\“ag_error_metadata\”\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m–> 994\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 995\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 996\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;31mValueError\u001b[0m: in user code:\n\n /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:853 train_function *\n return step_function(self, iterator)\n /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:842 step_function **\n outputs = model.distribute_strategy.run(run_step, args

data,))\n /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run\n return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica\n return self._call_for_each_replica(fn, args, kwargs)\n /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica\n return fn(*args, **kwargs)\n /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:835 run_step **\n outputs = model.train_step(data)\n /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:787 train_step\n y_pred = self(x, training=True)\n /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1037 __call__\n outputs = call_fn(inputs, *args, **kwargs)\n /usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:369 call\n return super(Sequential, self).call(inputs, training=training, mask=mask)\n /usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:415 call\n inputs, training=training, mask=mask)\n /usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:550 _run_internal_graph\n outputs = node.layer(*args, **kwargs)\n /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1020 __call__\n input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)\n /usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py:254 assert_input_compatibility\n ‘ but received input with shape ’ + display_shape(x.shape))\n\n ValueError: Input 0 of layer dense_6 is incompatible with the layer: expected axis -1 of input shape to have value 784 but received input with shape (None, 1200)\n“
]
}
]
},
{
”cell_type“: ”code“,
”metadata“: {
”id“: ”LWTisYLQM1aM“,
”colab“: {
”base_uri“: ”https://localhost:8080/“,
”height“: 1000
},
”outputId“: ”025f196a-8b36-4291-a63b-c457715f4268“
},
”source“: [
”import tensorflow as tf\n“,
”import keras_preprocessing\n“,
”from keras_preprocessing import image\n“,
”from keras_preprocessing.image import ImageDataGenerator\n“,
”\n“,
”TRAINING_DIR = \“/tmp/rps/\”\n“,
”# YOUR CODE HERE\n“,
”# START\n“,
”# Data Agumentation by ImageDataGenerator, you need rotation, flip horizental, shearing, focus, etc.\n“,
”\n“,
”train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode=\“nearest\”)\n“,
”\n“,
”# END\n“,
”VALIDATION_DIR = \“/tmp/rps-test-set/\”\n“,
”validation_datagen = ImageDataGenerator(rescale = 1./255)\n“,
”\n“,
”train_generator = train_datagen.flow_from_directory(\n“,
”\tTRAINING_DIR,\n“,
”\ttarget_size

150,150),\n“,
”\tclass_mode='categorical'\n“,
”)\n“,
”\n“,
”validation_generator = validation_datagen.flow_from_directory(\n“,
”\tVALIDATION_DIR,\n“,
”\ttarget_size

150,150),\n“,
”\tclass_mode='categorical'\n“,
”)\n“,
”\n“,
”model = tf.keras.models.Sequential([\n“,
” # YOUR CODE HERE\n“,
” # BEGIN\n“,
” # Note the input shape is the desired size of the image 150x150 with 3 bytes color\n“,
” tf.keras.layers.Conv2D(16, (3,3), activation=\“relu\”, input_shape

150, 150, 3)), \n“,
” # This is the first convolution, 64 neurons\n“,
” # MaxPooling layer (2, 2)\n“,
”\n“,
” tf.keras.layers.Conv2D(64, (3,3), activation=\“relu\”),\n“,
” tf.keras.layers.MaxPool2D(2, 2),\n“,
”\n“,
” # The second convolution, you can test different number of neurons\n“,
” # MaxPooling layer (2, 2)\n“,
”\n“,
” tf.keras.layers.Conv2D(16, (3,3), activation=\“relu\”),\n“,
” tf.keras.layers.MaxPool2D(2, 2),\n“,
”\n“,
” # The third convolution, 128 neurons\n“,
” # MaxPooling layer (2, 2)\n“,
”\n“,
” tf.keras.layers.Conv2D(128, (3,3), activation=\“relu\”),\n“,
” tf.keras.layers.MaxPool2D(2, 2),\n“,
”\n“,
” # The fourth convolution, you can test different number of neurons\n“,
” # MaxPooling layer (2, 2)\n“,
”\n“,
” tf.keras.layers.Conv2D(32, (3,3), activation=\“relu\”),\n“,
” tf.keras.layers.MaxPool2D(2, 2),\n“,
”\n“,
” # You can add more Conv and pooling layers here (Optional)\n“,
”\n“,
” # Flatten the results to feed into a DNN\n“,
”\n“,
” tf.keras.layers.Flatten(),\n“,
”\n“,
” # 512 neurons hidden layer\n“,
” \n“,
” tf.keras.layers.Dense(512, activation=\“relu\”),\n“,
”\n“,
” # An output layer for a multiclass problem\n“,
” \n“,
” tf.keras.layers.Dense(1, activation=\“sigmoid\”)\n“,
”\n“,
” # END\n“,
”])\n“,
”\n“,
”\n“,
”model.summary()\n“,
”\n“,
”# YOUR CODE HERE\n“,
”# BEGIN\n“,
”# Compile Model, try different optimizers, metrics=\n“,
”model.compile(loss = \“categorical_crossentropy\”, optimizer=\“rmsprop\”, metrics

\n“,
”# Train the model, use myCallback for acc > 0.98, verbose=1\n“,
”# YOUR CODE HERE\n“,
”\n“,
”class myCallback(tf.keras.callbacks.Callback):\n“,
” def on_epoch_end(self, epoch, logs={}):\n“,
” if(logs.get('accuracy')>0.98):\n“,
” print(\“\\nReached 98% accuracy so cancelling training!\”)\n“,
” self.model.stop_training = True\n“,
”\n“,
”callbacks=myCallback()\n“,
”\n“,
”history=model.fit_generator(train_generator, epochs=2, validation_data=validation_generator, verbose=1, callbacks

\n“,
”\n“,
”# END\n“,
”\n“,
”model.save(\“rps.h5\”)\n“
],
”execution_count“: null,
”outputs“: [
{
”output_type“: ”stream“,
”name“: ”stdout“,
”text“: [
”Found 2520 images belonging to 3 classes.\n“,
”Found 372 images belonging to 3 classes.\n“,
”Model: \“sequential_1\”\n“,
”_________________________________________________________________\n“,
”Layer (type) Output Shape Param # \n“,
”=================================================================\n“,
”conv2d (Conv2D) (None, 148, 148, 16) 448 \n“,
”_________________________________________________________________\n“,
”conv2d_1 (Conv2D) (None, 146, 146, 64) 9280 \n“,
”_________________________________________________________________\n“,
”max_pooling2d (MaxPooling2D) (None, 73, 73, 64) 0 \n“,
”_________________________________________________________________\n“,
”conv2d_2 (Conv2D) (None, 71, 71, 16) 9232 \n“,
”_________________________________________________________________\n“,
”max_pooling2d_1 (MaxPooling2 (None, 35, 35, 16) 0 \n“,
”_________________________________________________________________\n“,
”conv2d_3 (Conv2D) (None, 33, 33, 128) 18560 \n“,
”_________________________________________________________________\n“,
”max_pooling2d_2 (MaxPooling2 (None, 16, 16, 128) 0 \n“,
”_________________________________________________________________\n“,
”conv2d_4 (Conv2D) (None, 14, 14, 32) 36896 \n“,
”_________________________________________________________________\n“,
”max_pooling2d_3 (MaxPooling2 (None, 7, 7, 32) 0 \n“,
”_________________________________________________________________\n“,
”flatten_1 (Flatten) (None, 1568) 0 \n“,
”_________________________________________________________________\n“,
”dense_2 (Dense) (None, 512) 803328 \n“,
”_________________________________________________________________\n“,
”dense_3 (Dense) (None, 1) 513 \n“,
”=================================================================\n“,
”Total params: 878,257\n“,
”Trainable params: 878,257\n“,
”Non-trainable params: 0\n“,
”_________________________________________________________________\n“
]
},
{
”output_type“: ”stream“,
”name“: ”stderr“,
”text“: [
”/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1972: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n“,
” warnings.warn('`Model.fit_generator` is deprecated and '\n“
]
},
{
”output_type“: ”stream“,
”name“: ”stdout“,
”text“: [
”Epoch 1/2\n“
]
},
{
”output_type“: ”error“,
”ename“: ”InvalidArgumentError“,
”evalue“: ”ignored“,
”traceback“: [
”\u001b[0;31m—————————————————————————\u001b[0m“,
”\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)“,
”\u001b[0;32m<ipython-input-6-f3a8c5937259>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;31m# END\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1987\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1988\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1989\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 1990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1991\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdoc_controls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_not_generate_docs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1182\u001b[0m _r=1):\n\u001b[1;32m 1183\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1185\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 884\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m–> 885\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[0;31m# stateless function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m–> 950\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 951\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiltered_flat_args\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3038\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m 3039\u001b[0m return graph_function._call_flat(\n\u001b[0;32m-> 3040\u001b[0;31m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m 3041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3042\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1962\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1963\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1964\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1965\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1966\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 594\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m–> 596\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 597\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 598\u001b[0m outputs = execute.execute_with_cancellation(\n“,
”\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m—> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;31mInvalidArgumentError\u001b[0m: In mismatch In shape: 3 vs. 1: 0 0\n\t [] \n\nFunction call stack:\ntrain_function\n“
]
}
]
},
{
”cell_type“: ”code“,
”metadata“: {
”id“: ”aeTRVCr6aosw“,
”colab“: {
”base_uri“: ”https://localhost:8080/“,
”height“: 231
},
”outputId“: ”427493ff-ed8c-432d-84ff-6bd095409f67“
},
”source“: [
”import matplotlib.pyplot as plt\n“,
”acc = history.history\n“,
”val_acc = history.history\n“,
”loss = history.history\n“,
”val_loss = history.history\n“,
”\n“,
”epochs = range(len(acc))\n“,
”\n“,
”plt.plot(epochs, acc, ‘r’, label='Training accuracy')\n“,
”plt.plot(epochs, val_acc, ‘b’, label='Validation accuracy')\n“,
”plt.title('Training and validation accuracy')\n“,
”plt.legend(loc=0)\n“,
”plt.figure()\n“,
”\n“,
”\n“,
”plt.show()“
],
”execution_count“: null,
”outputs“: [
{
”output_type“: ”error“,
”ename“: ”NameError“,
”evalue“: ”ignored“,
”traceback“: [
”\u001b[0;31m—————————————————————————\u001b[0m“,
”\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)“,
”\u001b[0;32m<ipython-input-15-8bc21a7567ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m—-> 2\u001b[0;31m \u001b[0macc\u001b[0m \u001b\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mval_acc\u001b[0m \u001b\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mloss\u001b[0m \u001b\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mval_loss\u001b[0m \u001b\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n“,
”\u001b[0;31mNameError\u001b[0m: name ‘history’ is not defined“
]
}
]
},
{
”cell_type“: ”code“,
”metadata“: {
”id“: ”ZABJp7T3VLCU“,
”colab“: {
”resources“: {
”http://localhost:8080/nbextensions/google.colab/files.js“: {
”data“: ”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“,
”ok“: true,
”headers“: [
[
”content-type“,
”application/javascript“
]
],
”status“: 200,
”status_text“: ”“
}
},
”base_uri“: ”https://localhost:8080/“,
”height“: 38
},
”outputId“: ”23982d58-74c7-4bbc-b1db-14a57f951a45“
},
”source“: [
”import numpy as np\n“,
”from google.colab import files\n“,
”from keras.preprocessing import image\n“,
”\n“,
”uploaded = files.upload()\n“,
”\n“,
”for fn in uploaded.keys():\n“,
” \n“,
” # predicting images\n“,
” path = fn\n“,
” img = image.load_img(path, target_size

150, 150))\n“,
” x = image.img_to_array(img)\n“,
” x = np.expand_dims(x, axis=0)\n“,
”\n“,
” images = np.vstack()\n“,
” classes = model.predict(images, batch_size=10)\n“,
” print(fn)\n“,
” print(classes)“
],
”execution_count“: null,
”outputs“: [
{
”output_type“: ”display_data“,
”data“: {
”text/html“: [
”\n“,
” <input type=\“file\” id=\“files-ada9b336-8e57-4896-b00a-89dd924445eb\” name=\"files\“ multiple disabled\n”,
“ style=\”border:none\“ />\n”,
“ <output id=\”result-ada9b336-8e57-4896-b00a-89dd924445eb\“>\n”,
“ Upload widget is only available when the cell has been executed in the\n”,
“ current browser session. Please rerun this cell to enable.\n”,
“ </output>\n”,
“ <script src=\”/nbextensions/google.colab/files.js\“></script> ”
],
“text/plain”: [
“<IPython.core.display.HTML object>”
]
},
“metadata”: {}
}
]
}
]
}

py.user.next

Окт. 13, 2021 14:43:23

Используй тег code для точной передачи текста

[code]
Здесь пиши код
[/code]

PNicolai

Окт. 13, 2021 15:30:05

Вот файл

py.user.next

Окт. 13, 2021 22:29:07

PNicolai
Вот файл

Понятно.

Скинь коды, которые в файлах .py находятся.

PNicolai

Окт. 17, 2021 10:17:37

Нашел ошибку:

” tf.keras.layers.Dense(1, activation=\“sigmoid\”)\n“,

поменял на:

” tf.keras.layers.Dense(3, activation=\“sigmoid\”)\n“,

Работает!