我正在尝试将图像序列分为2类。每个序列有5帧。我已经将ConvLSTM2D用作第一层，并且遇到了以上错误。 input_shape参数为input_shape = (timesteps, rows, columns, channels)。
我生成的数据具有以下格式：
self.data = np.random.random((self.number_of_samples,
self.timesteps,
self.rows,
self.columns,
self.channels))
，第一层的实现如下所示：
model = Sequential()
# time distributed is used - working frame by frame
model.add(ConvLSTM2D(filters=10,
input_shape=input_shape,
kernel_size=(3, 3),
activation='relu',
data_format="channels_last"))
有人可以帮我吗？
编辑：这是我的玩具代码：
import numpy as np
from keras.layers import Dense, Dropout, LSTM
from keras.layers import Conv2D, Flatten, ConvLSTM2D
from keras.models import Sequential
from keras.layers.wrappers import TimeDistributed
import time
class Classifier():
"""Classifier model to classify image sequences"""
def __init__(self, number_of_samples, timesteps, rows, columns, channels, epochs, batch_size):
self.number_of_samples = number_of_samples
self.rows = rows
self.columns = columns
self.timesteps = timesteps
self.channels = channels
self.model = None
self.data = []
self.labels = []
self.epochs = epochs
self.batch_size = batch_size
self.X_train = []
self.X_test = []
self.y_train = []
self.y_test = []
def build_model(self, input_shape, output_label_size):
"""Builds the classification model
Keyword arguments:
input_shape -- shape of the image array
output_label_size -- 1
"""
# initialize a sequential model
model = Sequential()
# time distributed is used - working frame by frame
model.add(ConvLSTM2D(filters=10,
input_shape=input_shape,
kernel_size=(3, 3),
activation='relu',
data_format="channels_last"))
print("output shape 1:{}".format(model.output_shape))
print("correct till here")
model.add(Dropout(0.2))
model.add(ConvLSTM2D(filters=5,
kernel_size=(3, 3),
activation='relu'))
print("correct till here")
model.add(Dropout(0.2))
model.add(Flatten())
# print("output shape 2:{}".format(model.output_shape))
model.add(LSTM(10))
print("correct till here")
# print("output shape 3:{}".format(model.output_shape))
model.add(Dropout(0.2))
model.add(LSTM(5))
model.add(Dropout(0.2))
# print("output shape 4:{}".format(model.output_shape))
model.add(Dense(output_label_size,
kernel_initializer='uniform',
bias_initializer='zeros',
activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy')
print("correct till here")
# model.summary()
self.model = model
print("[INFO] Classifier model generated")
def split_data(self, data, labels):
"""Returns training and test set after splitting
Keyword arguments:
data -- image data
labels -- 0 or 1
"""
print("[INFO] split the data into training and testing sets")
train_test_split = 0.9
# split the data into train and test sets
split_index = int(train_test_split * self.number_of_samples)
# shuffled_indices = np.random.permutation(self.number_of_samples)
indices = np.arange(self.number_of_samples)
train_indices = indices[0:split_index]
test_indices = indices[split_index:]
X_train = data[train_indices, :, :]
X_test = data[test_indices, :, :]
y_train = labels[train_indices]
y_test = labels[test_indices]
print('Input shape: ', input_shape)
print('X_train shape: ', X_train.shape)
print('X_train[0] shape: ', X_train[0].shape)
print('X_train[0][0] shape: ', X_train[0][0].shape)
# print('y_train shape: ', y_train.shape)
# print('X_test shape: ', X_test.shape)
# print('y_test shape: ', y_test.shape)
return X_train, X_test, y_train, y_test
def load_training_data(self):
"""Load the training data for building the classification model."""
self.data = np.random.random((self.number_of_samples,
self.timesteps,
self.rows,
self.columns,
self.channels))
print("shape 1", type(self.data))
print("shape 2", type(self.data[0]))
print("shape 3", type(self.data[0][0]))
# self.labels = np.zeros(self.number_of_samples)
self.labels = np.ones(self.number_of_samples)
X_train, X_test, y_train, y_test = self.split_data(self.data, self.labels)
self.X_train = X_train
self.X_test = X_test
self.y_train = y_train
self.y_test = y_test
print("loading the training data done")
def train_model(self):
"""Train the model
Keyword arguments:
epochs -- number of training iterations
batch_size -- number of samples per batch
"""
self.model.fit(x=self.X_train,
y=self.y_train,
batch_size=self.batch_size,
epochs=self.epochs,
verbose=1,
validation_data=(self.X_test, self.y_test))
score = self.model.evaluate(self.X_test, self.y_test,
verbose=1, batch_size=self.batch_size)
prediction = self.model.predict(self.X_test,
batch_size=self.batch_size,
verbose=1)
print("Loss:{}".format(score))
print("Prediction:{}".format(prediction))
if __name__ == "__main__":
start = time.time()
number_of_samples = 12
# number_of_test_samples = 2000
timesteps = 5
rows = 14
columns = 14
channels = 3
output_label_size = 1
epochs = 1
batch_size = 1
input_shape = (timesteps, rows, columns, channels)
# input_shape = (batch_size, timesteps, rows, columns, channels)
classifier_model = Classifier(number_of_samples,
timesteps,
rows,
columns,
channels,
epochs,
batch_size)
classifier_model.load_training_data()
classifier_model.build_model(input_shape, output_label_size)
classifier_model.train_model()
end = time.time()
print("total time:{}".format(end - start))
答案 0 :(得分：3)
有几种方法可以指定input shape。从文档中：
将
input_shape参数传递给第一层。这是一个形状元组（整数或None项的元组，其中None表示可能期望任何正整数）。在input_shape中，不包括批次尺寸。
因此，正确的输入形状是：
input_shape = (timesteps, rows, columns, channels)
修复此错误后，您将遇到下一个错误（与input_shape相关的不）：
ValueError：输入0与层conv_lst_m2d_2不兼容：预期ndim = 5，发现ndim = 4
当您尝试添加第二个ConvLSTM2D层时，会发生此错误。发生这种情况是因为第一ConvLSTM2D层的输出是形状为(samples, output_row, output_col, filters)的4D张量。您可能需要设置return_sequences=True，在这种情况下，输出是形状为(samples, time, output_row, output_col, filters)的5D张量。
修复此错误后，您将在以下几行中遇到新的错误：
model.add(Flatten())
model.add(LSTM(10))
在Flatten层之前有LSTM层是没有意义的。这将永远无法使用，因为LSTM需要一个形状为(samples, time, input_dim)的3D输入张量。
总而言之，我强烈建议您仔细阅读Keras文档，尤其是LSTM和ConvLSTM2D层的文档。了解这些层如何有效利用它们也很重要。