I have a neural network, from a tf.data
data generator and a tf.keras
model, as follows (a simplified version-because it would be too long):
dataset = ...
A tf.data.Dataset
object that with the next_x
method calls the get_next
for the x_train
iterator and for the next_y
method calls the get_next
for the y_train
iterator. Each label is a (1, 67)
array in one-hot form.
Layers:
input_tensor = tf.keras.layers.Input(shape=(240, 240, 3)) # dim of x
output = tf.keras.layers.Flatten()(input_tensor)
output= tf.keras.Dense(67, activation='softmax')(output) # 67 is the number of classes
Model:
model = tf.keras.models.Model(inputs=input_tensor, outputs=prediction)
model.compile(optimizer=tf.train.AdamOptimizer(), loss=tf.losses.softmax_cross_entropy, metrics=['accuracy'])
model.fit_generator(gen(dataset.next_x(), dataset.next_y()), steps_per_epochs=100)
gen
is defined like this:
def gen(x, y):
while True:
yield(x, y)
My problem is that when I try to run it, I get an error in the model.fit
part:
ValueError: Cannot take the length of Shape with unknown rank.
Any ideas are appreciated!