Numpy und Keras Tensor Sizing Verwirrung

Question

Was auch immer ich ändere, dense_1_input will immer '(None, 296)'. Der Fehler ist:

ValueError: Error when checking model input: expected dense_1_input to have shape (None, 296) but got array with shape (296, 1) ` 

Code:

from keras.models import Sequential 
from keras.layers import Dense 
from random import randrange 
import numpy 
# fix random seed for reproducibility 
numpy.random.seed(7) 


def myGenerator(): 
    # load pima indians dataset 
    dataset = numpy.genfromtxt("vectorFile.csv", delimiter=",") 
    # split into input (X) and output (Y) variables 
    global X 
    global Y 
    X = dataset[:,0:148] 
    Y = dataset[:,149] 
    size = len(X) 

    while 1: 
     outputData = [] 
     outputAnswer = [] 

     for i in range(1):   
      firstPick = randrange(0,size) 
      firstResult = Y[firstPick] 
      firstPlayer = X[firstPick][0] 

      while True: 
       secondPick = randrange(0,size) 
       if firstPlayer==X[secondPick][0]: 
        break 

      if Y[firstPick]>Y[secondPick]: 
       outputAnswer.append([1,0]) 
      else: 
       outputAnswer.append([0,1]) 

      result = numpy.concatenate((X[firstPick], X[secondPick])) 
      result.reshape(1, 296) 
      outputData.append(result) 
     yield outputData,outputAnswer 

# create model 
model = Sequential() 

model.add(Dense(12, input_shape=(296,), activation='relu')) 
#model.add(Dense(12, input_dim=296, activation='relu')) 
model.add(Dense(8, activation='relu')) 
model.add(Dense(2, activation='sigmoid')) 
# Compile model 
model.compile(loss='binary_crossentropy', optimizer='adam', metrics= 
['accuracy']) 
# Fit the model 
#model.fit(X, Y, epochs=150, batch_size=10) 

#samples_per_epoch = batch_size * number_of_batches 
#samples_per_epoch = 100 * 1000 

model.fit_generator(myGenerator(), steps_per_epoch=5) 
# evaluate the model 
scores = model.evaluate(X, Y) 
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) 

Answer 1

ändern myGenerator() Funktion auf diese Weise:

result = result.reshape(1, 296) 

So wird das Ergebnis der reshape Betreiber gespeichert werden.