The Model.evaluate method checks the model's performance, usually on a validation set or test set. WARNING: All log messages before absl::InitializeLog() is called are written to STDERR Use the Model.fit method to adjust your model parameters and minimize the loss: model.fit(x_train, y_train, epochs=5) Set the optimizer class to adam, set the loss to the loss_fn function you defined earlier, and specify a metric to be evaluated for the model by setting the metrics parameter to accuracy. loss_fn(y_train, predictions).numpy()īefore you start training, configure and compile the model using Keras pile. This untrained model gives probabilities close to random (1/10 for each class), so the initial loss should be close to (1/10) ~= 2.3. This loss is equal to the negative log probability of the true class: The loss is zero if the model is sure of the correct class. The loss function takes a vector of ground truth values and a vector of logits and returns a scalar loss for each example. While this can make the model output more directly interpretable, this approach is discouraged as it's impossible to provide an exact and numerically stable loss calculation for all models when using a softmax output.ĭefine a loss function for training using losses.SparseCategoricalCrossentropy: loss_fn = tf.(from_logits=True) Note: It is possible to bake the tf.nn.softmax function into the activation function for the last layer of the network. The tf.nn.softmax function converts these logits to probabilities for each class: tf.nn.softmax(predictions).numpy()Īrray(], predictions = model(x_train).numpy()Īrray(], This model uses the Flatten, Dense, and Dropout layers.įor each example, the model returns a vector of logits or log-odds scores, one for each class. Most TensorFlow models are composed of layers. Layers are functions with a known mathematical structure that can be reused and have trainable variables. Sequential is useful for stacking layers where each layer has one input tensor and one output tensor. (x_train, y_train), (x_test, y_test) = mnist.load_data() This also converts the sample data from integers to floating-point numbers: mnist = tf. Scale these values to a range of 0 to 1 by dividing the values by 255.0. The pixel values of the images range from 0 through 255. Note: Make sure you have upgraded to the latest pip to install the TensorFlow 2 package if you are using your own development environment. If you are following along in your own development environment, rather than Colab, see the install guide for setting up TensorFlow for development. 02:20:34.136990: E external/local_xla/xla/stream_executor/cuda/cuda_:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 02:20:34.135478: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 02:20:34.135440: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered Print("TensorFlow version:", tf._version_) Import TensorFlow into your program to get started: import tensorflow as tf To run the code cells one at a time, hover over each cell and select the Run cell icon. To run all the code in the notebook, select Runtime > Run all.In Colab, connect to a Python runtime: At the top-right of the menu bar, select CONNECT.To follow this tutorial, run the notebook in Google Colab by clicking the button at the top of this page. Python programs are run directly in the browser-a great way to learn and use TensorFlow. This tutorial is a Google Colaboratory notebook. Build a neural network machine learning model that classifies images.
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