What should the generator return if it is used in a multi-model functional API?
Following this article, I'm trying to implement a generative RNN. In the mentioned article, the training and validation data are passed as fully loaded np.arrays. But I'm trying to use the model.fit_generator method and provide a generator instead.
I know that if it was a straightforward model, the generator should return:
def generator():
...
yield (samples, targets)
But this is a generative model which means there are two models involved:
encoder_inputs = Input(shape=(None,))
x = Embedding(num_encoder_tokens, embedding_dim)(encoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x, state_h, state_c = LSTM(embedding_dim, return_state=True)(x)
encoder_states = [state_h, state_c]
decoder_inputs = Input(shape=(None,))
x = Embedding(num_decoder_tokens, embedding_dim)(decoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x = LSTM(embedding_dim, return_sequences=True)(x, initial_state=encoder_states)
decoder_outputs = Dense(num_decoder_tokens, activation='softmax')(x)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
As mentioned before, I'm trying to use a generator:
model.fit_generator(generator(),
steps_per_epoch=500,
epochs=20,
validation_data=generator(),
validation_steps=val_steps)
But what should the generator() return? I'm a little confused since there are two input collections and one target.
python machine-learning keras generator rnn
edited Nov 26 '18 at 9:32
today
10.8k21837
asked Nov 26 '18 at 3:39
MehranMehran
3,925746112
add a comment |
Following this article, I'm trying to implement a generative RNN. In the mentioned article, the training and validation data are passed as fully loaded np.arrays. But I'm trying to use the model.fit_generator method and provide a generator instead.
I know that if it was a straightforward model, the generator should return:
def generator():
...
yield (samples, targets)
But this is a generative model which means there are two models involved:
encoder_inputs = Input(shape=(None,))
x = Embedding(num_encoder_tokens, embedding_dim)(encoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x, state_h, state_c = LSTM(embedding_dim, return_state=True)(x)
encoder_states = [state_h, state_c]
decoder_inputs = Input(shape=(None,))
x = Embedding(num_decoder_tokens, embedding_dim)(decoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x = LSTM(embedding_dim, return_sequences=True)(x, initial_state=encoder_states)
decoder_outputs = Dense(num_decoder_tokens, activation='softmax')(x)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
As mentioned before, I'm trying to use a generator:
model.fit_generator(generator(),
steps_per_epoch=500,
epochs=20,
validation_data=generator(),
validation_steps=val_steps)
But what should the generator() return? I'm a little confused since there are two input collections and one target.
python machine-learning keras generator rnn
edited Nov 26 '18 at 9:32
today
10.8k21837
asked Nov 26 '18 at 3:39
MehranMehran
3,925746112
add a comment |
Following this article, I'm trying to implement a generative RNN. In the mentioned article, the training and validation data are passed as fully loaded np.arrays. But I'm trying to use the model.fit_generator method and provide a generator instead.
I know that if it was a straightforward model, the generator should return:
def generator():
...
yield (samples, targets)
But this is a generative model which means there are two models involved:
encoder_inputs = Input(shape=(None,))
x = Embedding(num_encoder_tokens, embedding_dim)(encoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x, state_h, state_c = LSTM(embedding_dim, return_state=True)(x)
encoder_states = [state_h, state_c]
decoder_inputs = Input(shape=(None,))
x = Embedding(num_decoder_tokens, embedding_dim)(decoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x = LSTM(embedding_dim, return_sequences=True)(x, initial_state=encoder_states)
decoder_outputs = Dense(num_decoder_tokens, activation='softmax')(x)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
As mentioned before, I'm trying to use a generator:
model.fit_generator(generator(),
steps_per_epoch=500,
epochs=20,
validation_data=generator(),
validation_steps=val_steps)
But what should the generator() return? I'm a little confused since there are two input collections and one target.
python machine-learning keras generator rnn
edited Nov 26 '18 at 9:32
today
10.8k21837
asked Nov 26 '18 at 3:39
MehranMehran
3,925746112
Following this article, I'm trying to implement a generative RNN. In the mentioned article, the training and validation data are passed as fully loaded np.arrays. But I'm trying to use the model.fit_generator method and provide a generator instead.
I know that if it was a straightforward model, the generator should return:
def generator():
...
yield (samples, targets)
But this is a generative model which means there are two models involved:
encoder_inputs = Input(shape=(None,))
x = Embedding(num_encoder_tokens, embedding_dim)(encoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x, state_h, state_c = LSTM(embedding_dim, return_state=True)(x)
encoder_states = [state_h, state_c]
decoder_inputs = Input(shape=(None,))
x = Embedding(num_decoder_tokens, embedding_dim)(decoder_inputs)
x.set_weights([embedding_matrix])
x.trainable = False
x = LSTM(embedding_dim, return_sequences=True)(x, initial_state=encoder_states)
decoder_outputs = Dense(num_decoder_tokens, activation='softmax')(x)
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
As mentioned before, I'm trying to use a generator:
model.fit_generator(generator(),
steps_per_epoch=500,
epochs=20,
validation_data=generator(),
validation_steps=val_steps)
But what should the generator() return? I'm a little confused since there are two input collections and one target.
python machine-learning keras generator rnn
python machine-learning keras generator rnn
edited Nov 26 '18 at 9:32
today
10.8k21837
asked Nov 26 '18 at 3:39
MehranMehran
3,925746112
edited Nov 26 '18 at 9:32
today
10.8k21837
asked Nov 26 '18 at 3:39
MehranMehran
3,925746112
edited Nov 26 '18 at 9:32
today
10.8k21837
edited Nov 26 '18 at 9:32
today
10.8k21837
edited Nov 26 '18 at 9:32
today
10.8k21837
10.8k21837
asked Nov 26 '18 at 3:39
MehranMehran
3,925746112
asked Nov 26 '18 at 3:39
MehranMehran
3,925746112
asked Nov 26 '18 at 3:39
MehranMehran
3,925746112
3,925746112
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Since your model has two inputs and one output, the generator should return a tuple with two elements where the first element is a list containing two arrays, which corresponds to two input layers, and the second element is an array corresponding to output layer:
def generator():
...
yield [input_samples1, input_samples2], targets
Generally, in a model with M inputs and N outputs, the generator should return a tuple of two lists where the first one has M arrays and the second one has N arrays:
def generator():
...
yield [in1, in2, ..., inM], [out1, out2, ..., outN]
answered Nov 26 '18 at 9:30
todaytoday
10.8k21837
Thanks. I'll accept this once I get to verify it. Right now, my computer is busy with other stuff.
– Mehran
Nov 26 '18 at 14:12
Thanks for the answer, it worked. I just learned that you can also use the "named" layers and a dictionary. It would be great to add that to your answer to make it complete.
– Mehran
Nov 27 '18 at 14:03
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Since your model has two inputs and one output, the generator should return a tuple with two elements where the first element is a list containing two arrays, which corresponds to two input layers, and the second element is an array corresponding to output layer:
def generator():
...
yield [input_samples1, input_samples2], targets
Generally, in a model with M inputs and N outputs, the generator should return a tuple of two lists where the first one has M arrays and the second one has N arrays:
def generator():
...
yield [in1, in2, ..., inM], [out1, out2, ..., outN]
answered Nov 26 '18 at 9:30
todaytoday
10.8k21837
Thanks. I'll accept this once I get to verify it. Right now, my computer is busy with other stuff.
– Mehran
Nov 26 '18 at 14:12
Thanks for the answer, it worked. I just learned that you can also use the "named" layers and a dictionary. It would be great to add that to your answer to make it complete.
– Mehran
Nov 27 '18 at 14:03
add a comment |
Since your model has two inputs and one output, the generator should return a tuple with two elements where the first element is a list containing two arrays, which corresponds to two input layers, and the second element is an array corresponding to output layer:
def generator():
...
yield [input_samples1, input_samples2], targets
Generally, in a model with M inputs and N outputs, the generator should return a tuple of two lists where the first one has M arrays and the second one has N arrays:
def generator():
...
yield [in1, in2, ..., inM], [out1, out2, ..., outN]
answered Nov 26 '18 at 9:30
todaytoday
10.8k21837
Thanks. I'll accept this once I get to verify it. Right now, my computer is busy with other stuff.
– Mehran
Nov 26 '18 at 14:12
Thanks for the answer, it worked. I just learned that you can also use the "named" layers and a dictionary. It would be great to add that to your answer to make it complete.
– Mehran
Nov 27 '18 at 14:03
add a comment |
Since your model has two inputs and one output, the generator should return a tuple with two elements where the first element is a list containing two arrays, which corresponds to two input layers, and the second element is an array corresponding to output layer:
def generator():
...
yield [input_samples1, input_samples2], targets
Generally, in a model with M inputs and N outputs, the generator should return a tuple of two lists where the first one has M arrays and the second one has N arrays:
def generator():
...
yield [in1, in2, ..., inM], [out1, out2, ..., outN]
answered Nov 26 '18 at 9:30
todaytoday
10.8k21837
Since your model has two inputs and one output, the generator should return a tuple with two elements where the first element is a list containing two arrays, which corresponds to two input layers, and the second element is an array corresponding to output layer:
def generator():
...
yield [input_samples1, input_samples2], targets
Generally, in a model with M inputs and N outputs, the generator should return a tuple of two lists where the first one has M arrays and the second one has N arrays:
def generator():
...
yield [in1, in2, ..., inM], [out1, out2, ..., outN]
answered Nov 26 '18 at 9:30
todaytoday
10.8k21837
edited Nov 27 '18 at 10:43
answered Nov 26 '18 at 9:30
todaytoday
10.8k21837
answered Nov 26 '18 at 9:30
todaytoday
10.8k21837
answered Nov 26 '18 at 9:30
todaytoday
10.8k21837
10.8k21837
Thanks. I'll accept this once I get to verify it. Right now, my computer is busy with other stuff.
– Mehran
Nov 26 '18 at 14:12
Thanks for the answer, it worked. I just learned that you can also use the "named" layers and a dictionary. It would be great to add that to your answer to make it complete.
– Mehran
Nov 27 '18 at 14:03
add a comment |
Thanks. I'll accept this once I get to verify it. Right now, my computer is busy with other stuff.
– Mehran
Nov 26 '18 at 14:12
Thanks for the answer, it worked. I just learned that you can also use the "named" layers and a dictionary. It would be great to add that to your answer to make it complete.
– Mehran
Nov 27 '18 at 14:03
Thanks. I'll accept this once I get to verify it. Right now, my computer is busy with other stuff.
– Mehran
Nov 26 '18 at 14:12
Thanks. I'll accept this once I get to verify it. Right now, my computer is busy with other stuff.
– Mehran
Nov 26 '18 at 14:12
Thanks for the answer, it worked. I just learned that you can also use the "named" layers and a dictionary. It would be great to add that to your answer to make it complete.
– Mehran
Nov 27 '18 at 14:03
Thanks for the answer, it worked. I just learned that you can also use the "named" layers and a dictionary. It would be great to add that to your answer to make it complete.
– Mehran
Nov 27 '18 at 14:03
add a comment |
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