fit_generator expecting input for all layers of neural network?

Question

Newish to Keras and constructing a neural net with two dense layers. There's too much data to hold in memory, so I'm using the fit_generator function, but get the error ValueError: No data provided for "dense_2". Need data for each key in: ['dense_2'] ValueError: No data provided for "dense_2". Need data for each key in: ['dense_2'] . Small example below:

from keras.models import Sequential
from keras.layers import Dense
import numpy as np

model = Sequential([
    Dense(100, input_shape=(1924800,), activation='relu'),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

def generate_arrays_from_files(path, batch_size=50):
    while True:
        # Do things....
        yield ({'dense_1_input': np.asarray(outdata)}, {'output': np.asarray(outlabels)})

model.fit_generator(generate_arrays_from_files(path), steps_per_epoch=5, epochs=10)

Edit: forgot the compile line

Answer 1

You don't need to specify the layer in the input, and you obviously won't need to pass data to the second dense layer. Note that it's better to use a Keras generator, you can create a custom one like this or use a standard one .

You also need to compile your model.

from keras.models import Sequential
from keras.layers import Dense
import numpy as np

model = Sequential([
    Dense(100, input_shape=(1924800,), activation='relu'),
    Dense(1, activation='sigmoid')
])

optimizer = keras.optimizers.Adam(lr=1e-3)
model.compile(loss='binary_crossentropy',
              optimizer=optimizer,
              metrics=['accuracy'])

def generate_arrays_from_files(path, batch_size=50):
    while True:
        # Do things....
        yield np.asarray(outdata), np.asarray(outlabels)

model.fit_generator(generate_arrays_from_files(path), steps_per_epoch=5, epochs=10)

Is it normal to feed a vector of (1924800,) to the model by the way?