def test_tfdataset_with_tf_data_dataset_which_requires_table(self):
keys = [1, 0, -1]
dataset = tf.data.Dataset.from_tensor_slices([1, 2, -1, 5] * 40)
table = tf.contrib.lookup.HashTable(
initializer=tf.contrib.lookup.KeyValueTensorInitializer(
keys=keys, values=list(reversed(keys))),
default_value=100)
dataset = dataset.map(table.lookup)
def transform(x):
float_x = tf.to_float(x)
return float_x, 1
dataset = dataset.map(transform)
dataset = TFDataset.from_tf_data_dataset(dataset, batch_size=16)
seq = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=()),
tf.keras.layers.Dense(10, activation="softmax")
])
seq.compile(optimizer=tf.keras.optimizers.RMSprop(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model = KerasModel(seq)
model.fit(dataset)
def input_function():
ds = tf.data.Dataset.from_tensor_slices((dict(data_df), ))
ds = TFDataset.from_tf_data_dataset(
dataset=ds,
batch_size=batch_size,
batch_per_thread=batch_per_thread)
return ds
def input_fn():
def map_func(data):
image = data['image']
label = data['label']
one_hot_label = tf.one_hot(label, depth=10)
noise = tf.random.normal(mean=0.0, stddev=1.0, shape=(NOISE_DIM, ))
generator_inputs = (noise, one_hot_label)
discriminator_inputs = ((tf.to_float(image) / 255.0) - 0.5) * 2
return (generator_inputs, discriminator_inputs)
ds = tfds.load("mnist", split="train")
ds = ds.map(map_func)
dataset = TFDataset.from_tf_data_dataset(ds, batch_size=56)
return dataset
def test_tfdataset_with_tf_data_dataset(self):
dataset = tf.data.Dataset.from_tensor_slices(
(np.random.randn(102, 28, 28,
1), np.random.randint(0, 10, size=(102, ))))
dataset = dataset.map(lambda feature, label:
(tf.to_float(feature), label))
dataset = TFDataset.from_tf_data_dataset(dataset, batch_size=16)
seq = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
tf.keras.layers.Dense(10, activation="softmax")
])
seq.compile(optimizer=tf.keras.optimizers.RMSprop(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model = KerasModel(seq)
model.fit(dataset)
dataset = tf.data.Dataset.from_tensor_slices(
(np.random.randn(102, 28, 28,
1), np.random.randint(0, 10, size=(102, ))))
dataset = dataset.map(lambda feature, label:
(tf.to_float(feature), label))
dataset = TFDataset.from_tf_data_dataset(dataset, batch_per_thread=16)
model.evaluate(dataset)
def test_tfdataset_with_tf_data_dataset_which_contains_bool(self):
dataset = tf.data.Dataset.from_tensor_slices(
(np.random.randn(102, 28, 28,
1), np.random.randint(0, 10, size=(102, )),
np.ones(shape=(102, 28, 28, 1), dtype=np.bool)))
dataset = TFDataset.from_tf_data_dataset(dataset, batch_size=16)
feature, labels, mask = dataset.tensors
float_mask = tf.to_float(mask)
masked_feature = tf.to_float(feature) * float_mask
flatten = tf.layers.flatten(masked_feature)
logits = tf.layers.dense(flatten, 10)
loss = tf.reduce_mean(
tf.losses.sparse_softmax_cross_entropy(logits=logits,
labels=labels))
opt = TFOptimizer.from_loss(loss, Adam())
opt.optimize()
def input_function():
ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))
ds = ds.shuffle(1000)
ds = TFDataset.from_tf_data_dataset(dataset=ds,
batch_size=batch_size)
return ds
