def test():
model_to_be_restored = MLP()
checkpoint = tf.train.Checkpoint(myAwesomeModel=model_to_be_restored)
checkpoint.restore(tf.train.latest_checkpoint('./save'))
y_pred = np.argmax(model_to_be_restored.predict(data_loader.test_data),
axis=-1)
print("test accuracy: %f" %
(sum(y_pred == data_loader.test_label) / data_loader.num_test_data))
X, y = data_loader.get_batch(batch_size)
with tf.GradientTape() as tape:
y_pred = model(X)
loss = tf.keras.losses.sparse_categorical_crossentropy(
y_true=y, y_pred=y_pred)
loss = tf.reduce_mean(loss)
print("batch %d: loss %f" % (batch_index, loss.numpy()))
grads = tape.gradient(loss, model.variables)
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))
sparse_categorical_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
num_batches = int(data_loader.num_test_data // batch_size)
for batch_index in range(num_batches):
start_index, end_index = batch_index * batch_size, (batch_index +
1) * batch_size
y_pred = model.predict(data_loader.test_data[start_index:end_index])
sparse_categorical_accuracy.update_state(
y_true=data_loader.test_label[start_index:end_index],
y_pred=y_pred)
print("test accuracy: %f" % sparse_categorical_accuracy.result())
if training_loop == 'graph':
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)
num_batches = int(data_loader.num_train_data // batch_size * num_epochs)
# 建立计算图
X_placeholder = tf.compat.v1.placeholder(name='X',
shape=[None, 28, 28, 1],
dtype=tf.float32)
y_placeholder = tf.compat.v1.placeholder(name='y',
shape=[None],
dtype=tf.int32)
y_pred = model(X_placeholder)