我正在尝试获取 Keras 中中间层的输出,以下是我的代码:
XX = model.input # Keras Sequential() model object
YY = model.layers[0].output
F = K.function([XX], [YY]) # K refers to keras.backend
Xaug = X_train[:9]
Xresult = F([Xaug.astype('float32')])
运行这个,我得到一个错误:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'dropout_1/keras_learning_phase' with dtype bool
我开始知道,因为我在模型中使用了 dropout 层,所以我必须指定一个learning_phase()
根据 keras 标记我的函数文档 https://keras.io/getting-started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer。
我将代码更改为以下内容:
XX = model.input
YY = model.layers[0].output
F = K.function([XX, K.learning_phase()], [YY])
Xaug = X_train[:9]
Xresult = F([Xaug.astype('float32'), 0])
现在我遇到了一个我无法弄清楚的新错误:
TypeError: Cannot interpret feed_dict key as Tensor: Can not convert a int into a Tensor.
任何帮助,将不胜感激。
PS:我是 TensorFlow 和 Keras 的新手。
Edit 1 :以下是我正在使用的完整代码。我正在使用此 NIPS 中讨论的空间变换器网络paper https://papers.nips.cc/paper/5854-spatial-transformer-networks.pdf这是 Kera 的实现here https://github.com/oarriaga/spatial_transformer_networks/tree/master/src
input_shape = X_train.shape[1:]
# initial weights
b = np.zeros((2, 3), dtype='float32')
b[0, 0] = 1
b[1, 1] = 1
W = np.zeros((100, 6), dtype='float32')
weights = [W, b.flatten()]
locnet = Sequential()
locnet.add(Convolution2D(64, (3, 3), input_shape=input_shape, padding='same'))
locnet.add(Activation('relu'))
locnet.add(Convolution2D(64, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(MaxPooling2D(pool_size=(2, 2)))
locnet.add(Convolution2D(128, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(Convolution2D(128, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(MaxPooling2D(pool_size=(2, 2)))
locnet.add(Convolution2D(256, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(Convolution2D(256, (3, 3), padding='same'))
locnet.add(Activation('relu'))
locnet.add(MaxPooling2D(pool_size=(2, 2)))
locnet.add(Dropout(0.5))
locnet.add(Flatten())
locnet.add(Dense(100))
locnet.add(Activation('relu'))
locnet.add(Dense(6, weights=weights))
model = Sequential()
model.add(SpatialTransformer(localization_net=locnet,
output_size=(128, 128), input_shape=input_shape))
model.add(Convolution2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Convolution2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
#==============================================================================
# Start Training
#==============================================================================
#define training results logger callback
csv_logger = keras.callbacks.CSVLogger(training_logs_path+'.csv')
model.fit(X_train, y_train,
batch_size=batch_size,
epochs=20,
validation_data=(X_valid, y_valid),
shuffle=True,
callbacks=[SaveModelCallback(), csv_logger])
#==============================================================================
# Visualize what Transformer layer has learned
#==============================================================================
XX = model.input
YY = model.layers[0].output
F = K.function([XX, K.learning_phase()], [YY])
Xaug = X_train[:9]
Xresult = F([Xaug.astype('float32'), 0])
# input
for i in range(9):
plt.subplot(3, 3, i+1)
plt.imshow(np.squeeze(Xaug[i]))
plt.axis('off')
for i in range(9):
plt.subplot(3, 3, i + 1)
plt.imshow(np.squeeze(Xresult[0][i]))
plt.axis('off')