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Fixed the code in transfer_learning_with_hub.ipynb #2344

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Fixed the code in transfer_learning_with_hub.ipynb #2344

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43d2b11
Update transfer_learning_with_hub.ipynb
tilakrayal Dec 19, 2024
bcdf8e0
Update transfer_learning_with_hub.ipynb
tilakrayal Dec 19, 2024
ef90822
Update transfer_learning_with_hub.ipynb
tilakrayal Dec 19, 2024
6390ae8
Update transfer_learning_with_hub.ipynb
tilakrayal Dec 19, 2024
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28 changes: 15 additions & 13 deletions site/en/tutorials/images/transfer_learning_with_hub.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -90,13 +90,15 @@
},
"outputs": [],
"source": [
"pip install tf-keras\n",
"import numpy as np\n",
"import time\n",
"\n",
"import PIL.Image as Image\n",
"import matplotlib.pylab as plt\n",
"\n",
"import tensorflow as tf\n",
"import tf_keras as keras\n",
"import tensorflow_hub as hub\n",
"\n",
"import datetime\n",
Expand Down Expand Up @@ -150,7 +152,7 @@
"source": [
"IMAGE_SHAPE = (224, 224)\n",
"\n",
"classifier = tf.keras.Sequential([\n",
"classifier = keras.Sequential([\n",
" hub.KerasLayer(classifier_model, input_shape=IMAGE_SHAPE+(3,))\n",
"])"
]
Expand Down Expand Up @@ -181,7 +183,7 @@
},
"outputs": [],
"source": [
"grace_hopper = tf.keras.utils.get_file('image.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg')\n",
"grace_hopper = keras.utils.get_file('image.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg')\n",
"grace_hopper = Image.open(grace_hopper).resize(IMAGE_SHAPE)\n",
"grace_hopper"
]
Expand Down Expand Up @@ -261,7 +263,7 @@
},
"outputs": [],
"source": [
"labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')\n",
"labels_path = keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')\n",
"imagenet_labels = np.array(open(labels_path).read().splitlines())"
]
},
Expand Down Expand Up @@ -323,7 +325,7 @@
"source": [
"import pathlib\n",
"\n",
"data_file = tf.keras.utils.get_file(\n",
"data_file = keras.utils.get_file(\n",
" 'flower_photos.tgz',\n",
" 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',\n",
" cache_dir='.',\n",
Expand Down Expand Up @@ -353,7 +355,7 @@
"img_height = 224\n",
"img_width = 224\n",
"\n",
"train_ds = tf.keras.utils.image_dataset_from_directory(\n",
"train_ds = keras.utils.image_dataset_from_directory(\n",
" str(data_root),\n",
" validation_split=0.2,\n",
" subset=\"training\",\n",
Expand All @@ -362,7 +364,7 @@
" batch_size=batch_size\n",
")\n",
"\n",
"val_ds = tf.keras.utils.image_dataset_from_directory(\n",
"val_ds = keras.utils.image_dataset_from_directory(\n",
" str(data_root),\n",
" validation_split=0.2,\n",
" subset=\"validation\",\n",
Expand Down Expand Up @@ -419,7 +421,7 @@
},
"outputs": [],
"source": [
"normalization_layer = tf.keras.layers.Rescaling(1./255)\n",
"normalization_layer = keras.layers.Rescaling(1./255)\n",
"train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y)) # Where x—images, y—labels.\n",
"val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y)) # Where x—images, y—labels."
]
Expand Down Expand Up @@ -633,9 +635,9 @@
"source": [
"num_classes = len(class_names)\n",
"\n",
"model = tf.keras.Sequential([\n",
"model = keras.Sequential([\n",
" feature_extractor_layer,\n",
" tf.keras.layers.Dense(num_classes)\n",
" keras.layers.Dense(num_classes)\n",
"])\n",
"\n",
"model.summary()"
Expand Down Expand Up @@ -683,12 +685,12 @@
"outputs": [],
"source": [
"model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(),\n",
" loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" optimizer=keras.optimizers.Adam(),\n",
" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=['acc'])\n",
"\n",
"log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
"tensorboard_callback = tf.keras.callbacks.TensorBoard(\n",
"tensorboard_callback = keras.callbacks.TensorBoard(\n",
" log_dir=log_dir,\n",
" histogram_freq=1) # Enable histogram computation for every epoch."
]
Expand Down Expand Up @@ -846,7 +848,7 @@
},
"outputs": [],
"source": [
"reloaded = tf.keras.models.load_model(export_path)"
"reloaded = keras.models.load_model(export_path)"
]
},
{
Expand Down
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