1#完全采用 VGG 16 预先训练的模型2#载入套件3importtensorflow as tf4fromtensorflow.keras.applications.vgg16importVGG165fromtensorflow.keras.preprocessingimportimage6fromtensorflow.keras.applications.vgg16importpreprocess_input7fromtensorflow.keras.applications.vgg16importdecode_predictions8importnumpy as np910#载...
Successfully installed absl-py-0.7.1 astor-0.7.1 gast-0.2.2 grpcio-1.19.0 h5py-2.9.0 keras-applications-1.0.7 keras-preprocessing-1.0.9 markdown-3.0.1 mock-2.0.0 numpy-1.16.2 pbr-5.1.3 protobuf-3.7.0 setuptools-40.8.0 tensorboard-1.13.1 tensorflow-1.13.1 tensorflow-estimator-1.13.0 t...
Next we will look at how to add our model to FastAPI functionality. As a first step, we will add a response to the root of the URL and just simply return a message about our model in JSON format. Change the code in `async def root():` from “Hello world” to our message like t...
TensorFlow is a piece of cake, if you have ever used NumPy. One of the main differences between the two is that TensorFlow follows a "lazy" programming paradigm. It first builds all the action graphs to be completed, then it "runs" the graphs when a "session" is invoked. Building a c...
>>> import keras Using TensorFlow backend. >>> How to Install Keras on Windows Before we installTensorflowand Keras, we should install Python, pip, and virtualenv. If you already installed these libraries, you should continue to the next step, otherwise do this: ...
keras.layers import Dense, Dropout, LSTM, Embedding, Bidirectional from tensorflow.keras.models import Sequential from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.utils import to_categorical from tensorflow.keras....
First, we need to import our required Python packages: # import the necessary packages from sklearn.preprocessing import LabelBinarizer from pyimagesearch.nn.conv import MiniVGGNet from tensorflow.keras.callbacks import ModelCheckpoint from tensorflow.keras.optimizers import SGD ...
fromsklearn.preprocessingimportStandardScaler sc=StandardScaler()X_train=sc.fit_transform(X_train)X_test=sc.transform(X_test) Copy Here, you start by importing theStandardScalerand calling an instance of it. You then use itsfit_transformmethod to scale the training ...
“Hello World” example from the Gradio documentation Creating a Gradio interface for a machine learning model is just as easy! All we need to do is combine the preprocessing and inference steps from above into a single function: import torchfrom torchvision import transformsfrom torch.autograd imp...
Import the necessary libraries that you will use to preprocess the data and create the model. importnumpyasnp importpandasaspd importtensorflowastf fromsklearn.model_selectionimporttrain_test_split fromsklearn.metricsimportaccuracy_score fromtensorflow.keras.preprocessing.textimportTokenizer ...