python - 有没有办法调试 TensorFlow 代码？另外，在解释代码时如何查看变量或对象中的内容？

Question

运行 Python 代码时，调试和跟踪变量和对象值非常容易，但在 tensorflow 中，很难看到幕后发生的情况。我知道 tensorflow 在图表中工作，你必须运行 session 。在解释代码时是否有更简单的方法来查看这些值？我附上了下面的屏幕截图，您可以在其中跟踪每个变量值，但是在 tensorflow 中我无法做到这一点。我已经很累了，我在 session 中使用了 tf.print() 和 tf.eval() 。

这是tensorflow的代码，我想查看Z3和predict_op

的值

def model(X_train, Y_train, X_test, Y_test, learning_rate=0.01,


ops.reset_default_graph()  # to be able to rerun the model without overwriting tf variables
tf.set_random_seed(1)  # to keep results consistent (tensorflow seed)
seed = 3  # to keep results consistent (numpy seed)
(m, n_H0, n_W0, n_C0) = X_train.shape
n_y = Y_train.shape[1]
costs = []  # To keep track of the cost

# Create Placeholders of the correct shape
X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)

# Initialize parameters
parameters = initialize_parameters()

# Forward propagation: Build the forward propagation in the tensorflow graph
Z3 = forward_propagation(X, parameters)

# Cost function: Add cost function to tensorflow graph
cost = compute_cost(Z3, Y)


# Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)


# Initialize all the variables globally
init = tf.global_variables_initializer()

# Start the session to compute the tensorflow graph
with tf.Session() as sess:

    # Run the initialization
    sess.run(init)

    # Do the training loop
    for epoch in range(num_epochs):

        minibatch_cost = 0.
        num_minibatches = int(m / minibatch_size)  # number of minibatches of size minibatch_size in the train set
        seed = seed + 1
        minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)

        for minibatch in minibatches:
            # Select a minibatch
            (minibatch_X, minibatch_Y) = minibatch
            # IMPORTANT: The line that runs the graph on a minibatch.
            # Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).

            _, temp_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})


            minibatch_cost += temp_cost / num_minibatches

        # Print the cost every epoch
        if print_cost == True and epoch % 5 == 0:
            print("Cost after epoch %i: %f" % (epoch, minibatch_cost))
        if print_cost == True and epoch % 1 == 0:
            costs.append(minibatch_cost)



    # Calculate the correct predictions
    predict_op = tf.argmax(Z3, 1)
    correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1))
    # Calculate accuracy on the test set
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print(accuracy)
    train_accuracy = accuracy.eval({X: X_train, Y: Y_train})
    test_accuracy = accuracy.eval({X: X_test, Y: Y_test})
    print("Train Accuracy:", train_accuracy)
    print("Test Accuracy:", test_accuracy)

    return train_accuracy, test_accuracy, parameters

Answer 1

您可以尝试 TensorFlow Eager Execution，它允许您直接运行 TensorFlow 代码，而无需构建图表并在 session 期间运行它。它甚至在描述中说它可以更轻松地进行调试。

https://www.tensorflow.org/guide/eager