How do i pass my input/output to this network? - python-2.7

Question

I seem to have some problems starting my learning... I am not sure why..
the network is multi input (72 1d arrays) and output is a 1d array length 24. the 1d array output consist of numbers related to 145 different classes.
So: 72 inputs => 24 outputs
Minimal working example - without the input/output being set.
import keras
from keras.utils import np_utils
from keras import metrics
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten
from keras.layers import Conv1D,Conv2D, MaxPooling2D, MaxPooling1D, Reshape, ZeroPadding2D
from keras.utils import np_utils
from keras.layers.advanced_activations import LeakyReLU, PReLU
from keras.layers.advanced_activations import ELU
from keras.models import Model
from keras.layers import Input, Dense
from keras.layers import Dropout
from keras import backend as K
from keras.callbacks import ReduceLROnPlateau
from keras.callbacks import CSVLogger
from keras.callbacks import EarlyStopping
from keras.models import load_model
from keras.layers.merge import Concatenate
import numpy as np
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
nano_train_input = []
nano_train_output = []
nano_test_input = []
nano_test_output = []
## Creating train input:
for i in range(974):
nano_train_input.append(np.random.random((78,684,4)))
nano_train_output.append(np.random.randint(145,size=(228)).tolist())
## Creating test input:
for i in range(104):
nano_test_input.append(np.random.random((78,684,4)))
nano_test_output.append(np.random.randint(145,size=(228)).tolist())
def model(train_input, train_output, test_input, test_output, names=0):
# Paper uses dimension (40 x 45 =(15 * 3))
# Filter size 5
# Pooling size
# I use dimension (78 x 72 = (24 * 3)
# Filter size 9
print "In model"
i = 0
print_once = True
data_test_output = []
data_test_input = []
for matrix in test_input:
row,col,channel = matrix.shape
remove_output = (col/3)%24
remove_input = col%72
if remove_output > 0 :
test_output[i] = test_output[i][:-(remove_output)]
for split in chunks(test_output[i],24):
data_test_output.append(np.array(split))
if remove_input > 0:
out = np.split(matrix[:,:-(remove_input),:-1],matrix[:,:-(remove_input),:-1].shape[1]/72,axis=1)
else:
out = np.split(matrix[:,:,:-1],matrix[:,:,:-1].shape[1]/72,axis=1)
data_test_input.extend(out)
del out
i=i+1 # Increment
i=0
data_train_output = []
data_train_input = []
for matrix in train_input:
row,col,channel = matrix.shape
remove_output = (col/3)%24
remove_input = col%72
if remove_output > 0 :
train_output[i] = train_output[i][:-(remove_output)]
for split in chunks(train_output[i],24):
data_train_output.append(np.array(split))
if remove_input > 0:
out = np.split(matrix[:,:-(remove_input),:-1],matrix[:,:-(remove_input),:-1].shape[1]/72,axis=1)
else:
out = np.split(matrix[:,:,:-1],matrix[:,:,:-1].shape[1]/72,axis=1)
data_train_input.extend(out)
del out
i=i+1 # Increment
print
print "Len:"
print len(data_train_input)
print len(data_train_output)
print len(data_test_input)
print len(data_test_output)
print
print "Type[0]:"
print type(data_train_input[0])
print type(data_train_output[0])
print type(data_test_input[0])
print type(data_test_output[0])
print
print "Type:"
print type(data_train_input)
print type(data_train_output)
print type(data_test_input)
print type(data_test_output)
print
print "shape of [0]:"
print data_train_input[0].shape
print data_train_output[0].shape
print data_test_input[0].shape
print data_test_output[0].shape
list_of_input = [Input(shape = (78,3)) for i in range(72)]
list_of_conv_output = []
list_of_max_out = []
for i in range(72):
list_of_conv_output.append(Conv1D(filters = 32 , kernel_size = 6 , padding = "same", activation = 'relu')(list_of_input[i]))
list_of_max_out.append(MaxPooling1D(pool_size=3)(list_of_conv_output[i]))
merge = keras.layers.concatenate(list_of_max_out)
reshape = Flatten()(merge)
dense1 = Dense(units = 500, activation = 'relu', name = "dense_1")(reshape)
dense2 = Dense(units = 250, activation = 'relu', name = "dense_2")(dense1)
dense3 = Dense(units = 24 , activation = 'softmax', name = "dense_3")(dense2)
model = Model(inputs = list_of_input , outputs = dense3)
model.compile(loss="categorical_crossentropy", optimizer="adam" , metrics = [metrics.sparse_categorical_accuracy])
reduce_lr=ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1, mode='auto', epsilon=0.01, cooldown=0, min_lr=0.000000000000000000001)
stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto')
print "Train!"
print model.summary()
hist_current = model.fit(x = ,
y = ,
shuffle=False,
validation_data=(,),
validation_split=0.1,
epochs=150000,
verbose=1,
callbacks=[reduce_lr,stop])
model(nano_train_input,nano_train_output,nano_test_input, nano_test_output)
The input and output is stored as a list of numpy.ndarrays.
This is a minimal working example.. how am I supposed to pass the input an output?

Answer 1

I would try:
merge = keras.layers.concatenate(list_of_max_out)
merge = Flatten()(merge) # or GlobalMaxPooling1D or GlobalAveragePooling1D
dense1 = Dense(500, activation = 'relu')(merge)
You probably want to apply something to transform your output from Convolutional layers. In order to do that - you need to squash the time / sequential dimension. In order to do that try techniques I provided.

Answer 2

If you take a look at your code and outputs you indeed have what you say: 24 outputs (data_train_outputs[0].shape). However, if you look at your layer output of Keras, you have this as output:
dense_3 (Dense) (None, 26, 145) 36395
I would say that this should be an array with shape (None, 24)....
I suggest you add a reshape layer to get the output you want to have!