如何在张量流中可视化估计量训练模型？

Question

I have created a model with 3 hidden layers and trained it with the specific data-set.

How can I visualize the Model, with the neuron connections and weights at each iteration.

Here is the snippet of the python code :

#<ALL IMPORT STATEMENTS>
MODEL_DIR = <model_name>

def make_estimator(model_dir):
 config = run_config.RunConfig(model_dir=model_dir)
 feat_cols = [tf.feature_column.numeric_column("x", shape=<number_of_feat_cols>)]
 return estimator.DNNClassifier(config=config, hidden_units=[<>,<>,<>],feature_columns=feat_cols,n_classes=2,optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.001))

data = pd.read_csv(<csv_file>)

feat_data = data.drop('Type',axis=1)
feat_data_matrix = feat_data.as_matrix()

labels = data['Type']
labels_matrix = labels.as_matrix()

deep_model = make_estimator(MODEL_DIR)
input_fn = estimator.inputs.numpy_input_fn(x={'x':feat_data_matrix}, y=labels_matrix, shuffle=True, batch_size=10, num_epochs=1000)
tr_steps = <step_size>
deep_model.train(input_fn=input_fn,steps=tr_steps)
print ("Training Done")

In the code above, I have not created any tensorflow session, without it where can I implement the TensorBoard APIs for visualizing the model ?

Answer 1

By using the Python API simply call the method tf.summary.FileWriter

Then if you load the file written by the SummaryWriter into TensorBoard, the graph is shown.

You have to load the graph like this:

# Launch the graph.
current_session = tf.Session()
current_session.run(init)

# Create a summary writer, add the 'graph' to the event file.
writer = tf.summary.FileWriter(<some-directory>, current_session.graph)

See here.