Package: estimator
A framework for quickly creating machine learning models using Estimator API of TensorFlow.InstallationGetting StartedExample: CNN MNIST ClassifierModel FunctionLicenseInstallationInstall TensorFlow:pipinstalltensorflowand run:pipinstallestimatorIt is recommended to use avirtual environment.Getting StartedfromestimatorimportModelimporttensorflowastf# Define the network architecture - layers, number of units, activations etc.defnetwork(inputs):hidden=tf.layers.Dense(units=64,activation=tf.nn.relu)(inputs)outputs=tf.layers.Dense(units=10)(hidden)returnoutputs# Configure the learning process - loss, optimizer, evaluation metrics etc.model=Model(network,loss='sparse_softmax_cross_entropy',optimizer=('GradientDescent',0.001),metrics=['accuracy'])# Train the model using training datamodel.train(x_train,y_train,epochs=30,batch_size=128)# Evaluate the model performance on test or validation dataloss_and_metrics=model.evaluate(x_test,y_test)# Use the model to make predictions for new datapredictions=model.predict(x)# or call the model directlypredictions=model(x)More configuration options are available:model=Model(network,loss='sparse_softmax_cross_entropy',optimizer=optimizer('GradientDescent',0.001),metrics=['accuracy'],model_dir='/tmp/my_model')You can also use custom functions for loss and metrics:defcustom_loss(labels,outputs):passdefcustom_metric(labels,outputs):passmodel=Model(network,loss=custom_loss,optimizer=('GradientDescent',0.001),metrics=['accuracy',custom_metric])Example: CNN MNIST ClassifierThis example is based on theMNIST exampleof TensorFlow:fromestimatorimportModel,GradientDescent,TRAINimporttensorflowastfdefnetwork(x,mode):x=tf.reshape(x,[-1,28,28,1])x=tf.layers.Conv2D(filters=32,kernel_size=[5,5],padding='same',activation=tf.nn.relu)(x)x=tf.layers.MaxPooling2D(pool_size=[2,2],strides=2)(x)x=tf.layers.Conv2D(filters=64,kernel_size=[5,5],padding='same',activation=tf.nn.relu)(x)x=tf.layers.MaxPooling2D(pool_size=[2,2],strides=2)(x)x=tf.layers.Flatten()(x)x=tf.layers.Dense(units=1024,activation=tf.nn.relu)(x)x=tf.layers.Dropout(rate=0.4)(x,training=mode==TRAIN)x=tf.layers.Dense(units=10)(x)returnx# Configure the learning processmodel=Model(network,loss='sparse_softmax_cross_entropy',optimizer=('GradientDescent',0.001))modeparameter specifies whether the model is used for training, evaluation or prediction.Model FunctionTo have more control, you may configure the model inside a function usingEstimatorclass:fromestimatorimportEstimator,PREDICTimporttensorflowastfdefmodel(features,labels,mode):# Define the network architecturehidden=tf.layers.Dense(units=64,activation=tf.nn.relu)(features)outputs=tf.layers.Dense(units=10)(hidden)predictions=tf.argmax(outputs,axis=1)# In prediction mode, simply return predictions without configuring learning processifmode==PREDICT:returnpredictions# Configure the learning process for training and evaluation modesloss=tf.losses.sparse_softmax_cross_entropy(labels,outputs)optimizer=tf.train.GradientDescentOptimizer(0.001)accuracy=tf.metrics.accuracy(labels,predictions)returndict(loss=loss,optimizer=optimizer,metrics={'accuracy':accuracy})# Create the model using model functionmodel=Estimator(model)# Train the modelmodel.train(x_train,y_train,epochs=30,batch_size=128)LicenseMIT