Why I'm getting "TypeError: Failed to convert object of type <type 'dict'> to Tensor."? - python-2.7

Question

I'm new to TF and ML.
Details about data: Features(x) - (70 x 70 x 70) tensor for each sample, y - a float for each sample.
TFRecords created with the following code:
def convert_to_tf_records():
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _float64_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
tfrecords_filename = 'A-100-h2-h2o.tfrecords'
writer = tf.python_io.TFRecordWriter(tfrecords_filename)
# Get data from db for now.
db = connect('results-60-70.db')
data = db.select(selection='Ti')
i = 0
for row in data:
desc = np.array(json.loads(row.descriptor), dtype=np.float32)
print(desc.shape)
be = float(row.binding_energy) * 23 # Convert to Kcal/mol ?
desc = desc.flatten()
desc = desc.tostring()
example = tf.train.Example(features=tf.train.Features(feature={'voxel_grid': _bytes_feature(desc), 'binding_energy': _float64_feature(be)}))
writer.write(example.SerializeToString())
i += 1
if i >= 10:
break
Input function:
def my_input_function(fname, perform_shuffle=False, repeat_count=None):
def _parse_elements(example):
features = tf.parse_single_example(example, features={'voxel_grid': tf.FixedLenFeature([], tf.string), 'binding_energy': tf.FixedLenFeature([], tf.float32)})
vg = tf.decode_raw(features['voxel_grid'], tf.float32)
vg = tf.reshape(vg, [70, 70, 70])
vg = tf.convert_to_tensor(vg, dtype=tf.float32)
vg = {'voxel_grid': vg}
e = tf.cast(features['binding_energy'], tf.float32)
return vg, e
def input_function():
dataset = tf.data.TFRecordDataset(fname).map(_parse_elements)
dataset = dataset.repeat(repeat_count)
dataset = dataset.batch(5)
dataset = dataset.prefetch(1)
if perform_shuffle:
dataset.shuffle(20)
iterator = dataset.make_one_shot_iterator()
batch_features, batch_labels = iterator.get_next()
return batch_features, batch_labels
return input_function
Model function:
def my_model_function(features, labels, mode):
if mode == tf.estimator.ModeKeys.PREDICT:
tf.logging.info("my_model_fn: PREDICT, {}".format(mode))
elif mode == tf.estimator.ModeKeys.EVAL:
tf.logging.info("my_model_fn: EVAL, {}".format(mode))
elif mode == tf.estimator.ModeKeys.TRAIN:
tf.logging.info("my_model_fn: TRAIN, {}".format(mode))
feature_columns = [tf.feature_column.numeric_column('voxel_grid', shape=(70, 70, 70), dtype=tf.float32)]
# Create the layer of input
input_layer = tf.feature_column.input_layer(features, feature_columns)
input_layer = tf.reshape(input_layer, [-1, 70, 70, 70, 1])
# Convolution layers
conv1 = tf.layers.conv3d(inputs=input_layer, strides=(2, 2, 2), filters=32, kernel_size=(7, 7, 7))
conv2 = tf.layers.conv3d(inputs=conv1, strides=(2, 2, 2), filters=32, kernel_size=(7, 7, 7))
pool3 = tf.layers.max_pooling3d(inputs=conv2, pool_size=[2, 2, 2], strides=2)
flat = tf.layers.flatten(pool3)
dense1 = tf.layers.dense(inputs=flat, units=10, activation=tf.nn.relu)
dense2 = tf.layers.dense(inputs=dense1, units=10, activation=tf.nn.relu)
output = tf.layers.dense(inputs=dense2, units=1)
predictions = {'binding_energy': output}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate loss
loss = tf.losses.mean_squared_error(labels=labels, predictions=predictions)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# Add evaluation metrics
eval_metric_ops = {"mse": tf.metrics.mean_squared_error(labels=labels, predictions=predictions['binding_energy'])}
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
When calling model.train using
model = tf.estimator.Estimator(model_fn=my_model_function, model_dir='./model_dir')
model.train(input_fn=my_input_function('A-100-h2-h2o.tfrecords'), steps=100)
I get the following error.
TypeError: Failed to convert object of type to Tensor.

Answer 1

Found it!
changing
# Calculate loss
loss = tf.losses.mean_squared_error(labels=labels, predictions=predictions)
to
# Calculate loss
loss = tf.losses.mean_squared_error(labels=labels, predictions=predictions['binding_energy'])
solves the issue.