keras預期

Question

目前我正在試圖獲得fit_generator與我的發電機工作，但不知何故這不工作那麼好fit_generator不工作..

下面是一個例子：

import numpy as np 
from keras.utils import np_utils 
from keras import metrics 
import keras 
from keras.models import Sequential 
from keras.optimizers import SGD 
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten 
from keras.layers import Conv1D,Conv2D,MaxPooling2D, MaxPooling1D, Reshape 
from keras.utils import np_utils 
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 



def generator(batch_size): 

    global train_input 
    train_input = np.random.randint(5,size=(5000,33,45,8,3)) 
    global train_output 
    train_output = np.random.randint(5,size=(5000,15)) 
    global train_input_concat 
    train_input_concat = np.empty((0,33,45,8,3)) 

    while True: 
     for input in train_input: 
      input = np.expand_dims(input,axis=0) 
      train_input_concat = np.append(train_input_concat,input,axis=0) 
      print train_input_concat.shape 
      print input.shape 
      raw_input("something") 
      if (batch_size) == train_input_concat.shape[0]: 
       output_train_set = train_output[:batch_size,:] 
       train_output = np.delete(train_output,np.s_[:batch_size],axis=0) 
       train_output_set = np_utils.to_categorical(output_train_set,145) 
       train_input_set = train_input_concat 
       del train_input_concat 
       train_input_concat = np.empty((0,33,45,8,3)) 
       print train_input_set.shape 
       print train_output_set.shape 
       print train_output.shape 
       raw_input("Something yield") 
       yield train_input_set,train_output_set 

def model3(): 
    stride = 2 
    dim = 40 
    total_frames_with_deltas = 45 
    total_frames = 15 
    window_height = 8 
    splits = ((40-8)+1)/1 

    kernel_number = 150#int(math.ceil(splits)) 
    list_of_input = [Input(shape = (total_frames_with_deltas,window_height,3)) for i in range(splits)] 
    list_of_conv_output = [] 
    list_of_max_out = [] 
    for i in range(splits): 
     list_of_conv_output.append(Conv2D(filters = kernel_number , kernel_size = (15,6))(list_of_input[i])) 
     list_of_max_out.append((MaxPooling2D(pool_size=((2,2)))(list_of_conv_output[i]))) 

    merge = keras.layers.concatenate(list_of_max_out) 
    print merge.shape 
    reshape = Reshape((total_frames,-1))(merge) 

    dense1 = Dense(units = 1000, activation = 'relu', name = "dense_1")(reshape) 
    dense2 = Dense(units = 1000, activation = 'relu', name = "dense_2")(dense1) 
    dense3 = Dense(units = 145 , activation = 'softmax', name = "dense_3")(dense2) 
    #dense4 = Dense(units = 1, activation = 'linear', name = "dense_4")(dense3) 


    model = Model(inputs = list_of_input , outputs = dense3) 
    model.compile(loss="categorical_crossentropy", optimizer="SGD" , metrics = [metrics.categorical_accuracy]) 

    reduce_lr=ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1, mode='auto', epsilon=0.001, cooldown=0) 
    stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto') 

    print model.summary() 

    raw_input("okay?") 
    hist_current = model.fit_generator(generator(1), 
         steps_per_epoch=1, 
         epochs = 10, 
         verbose = 2, 
         validation_data = None) 
model3() 

這是正確的發電機發..

，因爲我收到錯誤消息：？

Traceback (most recent call last): 
    File "test_generator.py", line 90, in <module> 
    model3() 
    File "test_generator.py", line 89, in model3 
    validation_data = None) 
    File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 87, in wrapper 
    return func(*args, **kwargs) 
    File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1876, in fit_generator 
    class_weight=class_weight) 
    File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1614, in train_on_batch 
    check_batch_axis=True) 
    File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1295, in _standardize_user_data 
    exception_prefix='model input') 
    File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 100, in _standardize_input_data 
    'Found: array with shape ' + str(data.shape)) 
ValueError: The model expects 33 input arrays, but only received one array. Found: array with shape (1, 33, 45, 8, 3) 

我不明白，因爲它與33個輸入相同？那爲什麼不能讀它呢？

編輯：

與列表

這裏：

import numpy as np 
from keras.utils import np_utils 
from keras import metrics 
import keras 
from keras.models import Sequential 
from keras.optimizers import SGD 
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten 
from keras.layers import Conv1D,Conv2D,MaxPooling2D, MaxPooling1D, Reshape 
from keras.utils import np_utils 
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 



def generator(batch_size): 

    global train_input 
    train_input = np.random.randint(5,size=(5000,33,45,8,3)) 
    global train_output 
    train_output = np.random.randint(5,size=(5000,15)) 
    global train_input_concat 
    train_input_concat = np.empty((0,33,45,8,3)) 

    while True: 
     for input in train_input: 
      input = np.expand_dims(input,axis=0) 
      train_input_concat = np.append(train_input_concat,input,axis=0) 
      print train_input_concat.shape 
      print input.shape 
      raw_input("something") 
      if (batch_size) == train_input_concat.shape[0]: 
       output_train_set = train_output[:batch_size,:] 
       train_output = np.delete(train_output,np.s_[:batch_size],axis=0) 
       train_output_set = np_utils.to_categorical(output_train_set,145) 
       train_input_set = train_input_concat 
       del train_input_concat 
       train_input_concat = np.empty((0,33,45,8,3)) 
       print train_input_set.shape 
       print train_output_set.shape 
       print train_output.shape 
       input_list = np.split(train_input_set,33,axis=1) 
       print len(input_list) 
       yield ({'train_input': input_list},{'labels':train_output_set}) 

def model3(): 
    stride = 2 
    dim = 40 
    total_frames_with_deltas = 45 
    total_frames = 15 
    window_height = 8 
    splits = ((40-8)+1)/1 

    kernel_number = 150#int(math.ceil(splits)) 
    list_of_input = [Input(shape = (total_frames_with_deltas,window_height,3)) for i in range(splits)] 
    list_of_conv_output = [] 
    list_of_max_out = [] 
    for i in range(splits): 
     list_of_conv_output.append(Conv2D(filters = kernel_number , kernel_size = (15,6))(list_of_input[i])) 
     list_of_max_out.append((MaxPooling2D(pool_size=((2,2)))(list_of_conv_output[i]))) 

    merge = keras.layers.concatenate(list_of_max_out) 
    print merge.shape 
    reshape = Reshape((total_frames,-1))(merge) 

    dense1 = Dense(units = 1000, activation = 'relu', name = "dense_1")(reshape) 
    dense2 = Dense(units = 1000, activation = 'relu', name = "dense_2")(dense1) 
    dense3 = Dense(units = 145 , activation = 'softmax', name = "dense_3")(dense2) 
    #dense4 = Dense(units = 1, activation = 'linear', name = "dense_4")(dense3) 


    model = Model(inputs = list_of_input , outputs = dense3) 
    model.compile(loss="categorical_crossentropy", optimizer="SGD" , metrics = [metrics.categorical_accuracy]) 

    reduce_lr=ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1, mode='auto', epsilon=0.001, cooldown=0) 
    stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto') 

    print model.summary() 

    raw_input("okay?") 
    hist_current = model.fit_generator(generator(1), 
         steps_per_epoch=1, 
         epochs = 10, 
         verbose = 2, 
         validation_data = None) 
model3() 

這給了我錯誤消息：

Traceback (most recent call last): 
    File "test_generator.py", line 93, in <module> 
    model3() 
    File "test_generator.py", line 92, in model3 
    validation_data = None) 
    File "/usr/local/lib/python2.7/site-packages/keras/legacy/interfaces.py", line 88, in wrapper 
    return func(*args, **kwargs) 
    File "/usr/local/lib/python2.7/site-packages/keras/engine/training.py", line 1868, in fit_generator 
    batch_size = list(x.values())[0].shape[0] 
AttributeError: 'list' object has no attribute 'shape' 

Answer 1

假設這是一個模型中使用fit_generator（）的情況下，多個可能具有不同形狀的輸入，下面的每個樣本兩個輸入的自定義發生器示例可能有幫助：

def data_gen(input1_shape, input2_shape, batch_size): 
    while True: 
     input1_batch = [] 
     input2_batch = [] 
     labels_batch = [] 
     for i in range(batch_size): 
      # Assume inp1 and inp2 are two inputs for a sample 
      val = np.random.randint(0, 100) 
      inp1 = np.full(input1_shape, val) 
      inp2 = np.full(input2_shape, val) 

      # Get label 
      labels_batch.append(np.random.choice([0, 1])) 

      # Parallel lists for each input 
      input1_batch.append(inp1) 
      input2_batch.append(inp2) 

     # Yield the accumulated batch 
     yield [input1_batch, input2_batch], labels_batch