keras 使用 tensorflow 作为后端：无法将 feed_dict 键解释为 Tensor：无法将 int 转换为 Tensor
keras using tensorflow as backend :Cannot interpret feed_dict key as Tensor: Can not convert a int into a Tensor
我正在尝试在 keras 中使用 tensorboard。以下是我的代码：
from keras.layers import merge, Dropout, Convolution2D, MaxPooling2D, Input, Dense, Flatten, Merge
from keras.models import Model
from keras.callbacks import EarlyStopping, ReduceLROnPlateau,TensorBoard
import pickle
from sklearn.utils import shuffle
import numpy as np
import random
from keras.optimizers import Adam
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
np.random.seed(1000)
def load_pickled_data(file, columns):
with open(file, mode='rb') as f:
dataset = pickle.load(f)
return tuple(map(lambda c: dataset[c], columns))
train_preprocessed_dataset_file = "train.p"
test_preprocessed_dataset_file = "test.p"
X_train, y_train_64 = load_pickled_data(train_preprocessed_dataset_file, columns = ['features', 'labels'])
X_test, y_test_64 = load_pickled_data(test_preprocessed_dataset_file, columns = ['features', 'labels'])
y_train = y_train_64.astype(np.float32)
y_test = y_test_64.astype(np.float32)
old_session = KTF.get_session()
with tf.Graph().as_default():
session = tf.Session('')
KTF.set_session(session)
KTF.set_learning_phase(1)
###CNN model###
input_img = Input(shape=(32, 32, 1))
conv_1 = Convolution2D(32, 5, 5, border_mode='same', activation='relu')(input_img)
pool_1 = MaxPooling2D((2, 2))(conv_1)
pool_1 = Dropout(0.1)(pool_1)
conv_2 = Convolution2D(64, 5, 5, border_mode='same', activation='relu')(pool_1)
pool_2 = MaxPooling2D((2, 2))(conv_2)
pool_2 = Dropout(0.2)(pool_2)
conv_3 = Convolution2D(128, 5, 5, border_mode='same', activation='relu')(pool_2)
pool_3 = MaxPooling2D((2, 2))(conv_3)
pool_3 = Dropout(0.3)(pool_3)
pool_3 = Flatten()(pool_3)
pool_1 = MaxPooling2D((4, 4))(pool_1)
pool_1 = Flatten()(pool_1)
pool_2 = MaxPooling2D((2, 2))(pool_2)
pool_2 =Flatten()(pool_2)
all_features = merge([pool_1, pool_2, pool_3], mode='concat')
logits = Dense(500,activation='relu')(all_features)
logits = Dropout(0.5)(logits)
res = Dense(43,activation='softmax')(logits)
c_model = Model(input_img, res)
c_model.summary()
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
c_model.compile(loss='categorical_crossentropy', optimizer= adam, metrics=['accuracy'])
tensor_board = TensorBoard(log_dir='./logs', histogram_freq=1)
history = c_model.fit(X_train, y_train, batch_size=128,nb_epoch=3,shuffle=True,verbose=1,validation_split=0.25,callbacks=[tensor_board])
loss_and_metrics = c_model.evaluate(X_test, y_test, batch_size=128)
KTF.set_session(old_session)
但错误发生如下：
File
"/home/jasontian/enter/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py",
line 866, in runfile execfile(filename, namespace)
File
"/home/jasontian/enter/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py",
line 102, in execfile exec(compile(f.read(), filename, 'exec'),
namespace)
File "/media/jasontian/keras_tf.py", line 111, in history =
c_model.fit(X_train, y_train,
batch_size=128,nb_epoch=3,shuffle=True,verbose=1,validation_split=0.25,callbacks=[tensor_board])
File
"/home/jasontian/enter/lib/python3.5/site-packages/keras/engine/training.py",
line 1196, in fit initial_epoch=initial_epoch) File
"/home/jasontian/enter/lib/python3.5/site-packages/keras/engine/training.py",
line 911, in _fit_loop callbacks.on_epoch_end(epoch, epoch_logs)
File
"/home/jasontian/enter/lib/python3.5/site-packages/keras/callbacks.py",
line 76, in on_epoch_end callback.on_epoch_end(epoch, logs)
File
"/home/jasontian/enter/lib/python3.5/site-packages/keras/callbacks.py",
line 653, in on_epoch_end result = self.sess.run([self.merged],
feed_dict=feed_dict)
File
"/home/jasontian/enter/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 766, in run run_metadata_ptr)
File
"/home/jasontian/enter/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 921, in _run
+ e.args[0]) TypeError: Cannot interpret feed_dict key as Tensor: Can not convert a int into a Tensor.
起初我以为它可能是y_train.dtype（它是float64），但我发现它在一个例子中效果很好。
update:the X_train 的形状是 (39209,32,32,1)。
那么我该如何解决呢？
没有 tf session 不行吗？如果你真的不需要会话，你可以试试这个：
from keras.layers import merge, Dropout, Convolution2D, MaxPooling2D, Input, Dense, Flatten, Merge
from keras.models import Model
from keras.callbacks import EarlyStopping, ReduceLROnPlateau,TensorBoard
import pickle
from sklearn.utils import shuffle
import numpy as np
import random
from keras.optimizers import Adam
np.random.seed(1000)
def load_pickled_data(file, columns):
with open(file, mode='rb') as f:
dataset = pickle.load(f)
return tuple(map(lambda c: dataset[c], columns))
train_preprocessed_dataset_file = "train.p"
test_preprocessed_dataset_file = "test.p"
X_train, y_train_64 = load_pickled_data(train_preprocessed_dataset_file, columns = ['features', 'labels'])
X_test, y_test_64 = load_pickled_data(test_preprocessed_dataset_file, columns = ['features', 'labels'])
y_train = y_train_64.astype(np.float32)
y_test = y_test_64.astype(np.float32)
###CNN model###
input_img = Input(shape=(32, 32, 1))
conv_1 = Convolution2D(32, 5, 5, border_mode='same', activation='relu')(input_img)
pool_1 = MaxPooling2D((2, 2))(conv_1)
pool_1 = Dropout(0.1)(pool_1)
conv_2 = Convolution2D(64, 5, 5, border_mode='same', activation='relu')(pool_1)
pool_2 = MaxPooling2D((2, 2))(conv_2)
pool_2 = Dropout(0.2)(pool_2)
conv_3 = Convolution2D(128, 5, 5, border_mode='same', activation='relu')(pool_2)
pool_3 = MaxPooling2D((2, 2))(conv_3)
pool_3 = Dropout(0.3)(pool_3)
pool_3 = Flatten()(pool_3)
pool_1 = MaxPooling2D((4, 4))(pool_1)
pool_1 = Flatten()(pool_1)
pool_2 = MaxPooling2D((2, 2))(pool_2)
pool_2 =Flatten()(pool_2)
all_features = merge([pool_1, pool_2, pool_3], mode='concat')
logits = Dense(500,activation='relu')(all_features)
logits = Dropout(0.5)(logits)
res = Dense(43,activation='softmax')(logits)
c_model = Model(input_img, res)
c_model.summary()
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
c_model.compile(loss='categorical_crossentropy', optimizer= adam, metrics=['accuracy'])
tensor_board = TensorBoard(log_dir='./logs', histogram_freq=1)
history = c_model.fit(X_train, y_train, batch_size=128,nb_epoch=3,shuffle=True,verbose=1,validation_split=0.25,callbacks=[tensor_board])
loss_and_metrics = c_model.evaluate(X_test, y_test, batch_size=128)
如果您的 keras 默认后端是 Tensorflow，则不必指定它。
我正在尝试在 keras 中使用 tensorboard。以下是我的代码：
from keras.layers import merge, Dropout, Convolution2D, MaxPooling2D, Input, Dense, Flatten, Merge
from keras.models import Model
from keras.callbacks import EarlyStopping, ReduceLROnPlateau,TensorBoard
import pickle
from sklearn.utils import shuffle
import numpy as np
import random
from keras.optimizers import Adam
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
np.random.seed(1000)
def load_pickled_data(file, columns):
with open(file, mode='rb') as f:
dataset = pickle.load(f)
return tuple(map(lambda c: dataset[c], columns))
train_preprocessed_dataset_file = "train.p"
test_preprocessed_dataset_file = "test.p"
X_train, y_train_64 = load_pickled_data(train_preprocessed_dataset_file, columns = ['features', 'labels'])
X_test, y_test_64 = load_pickled_data(test_preprocessed_dataset_file, columns = ['features', 'labels'])
y_train = y_train_64.astype(np.float32)
y_test = y_test_64.astype(np.float32)
old_session = KTF.get_session()
with tf.Graph().as_default():
session = tf.Session('')
KTF.set_session(session)
KTF.set_learning_phase(1)
###CNN model###
input_img = Input(shape=(32, 32, 1))
conv_1 = Convolution2D(32, 5, 5, border_mode='same', activation='relu')(input_img)
pool_1 = MaxPooling2D((2, 2))(conv_1)
pool_1 = Dropout(0.1)(pool_1)
conv_2 = Convolution2D(64, 5, 5, border_mode='same', activation='relu')(pool_1)
pool_2 = MaxPooling2D((2, 2))(conv_2)
pool_2 = Dropout(0.2)(pool_2)
conv_3 = Convolution2D(128, 5, 5, border_mode='same', activation='relu')(pool_2)
pool_3 = MaxPooling2D((2, 2))(conv_3)
pool_3 = Dropout(0.3)(pool_3)
pool_3 = Flatten()(pool_3)
pool_1 = MaxPooling2D((4, 4))(pool_1)
pool_1 = Flatten()(pool_1)
pool_2 = MaxPooling2D((2, 2))(pool_2)
pool_2 =Flatten()(pool_2)
all_features = merge([pool_1, pool_2, pool_3], mode='concat')
logits = Dense(500,activation='relu')(all_features)
logits = Dropout(0.5)(logits)
res = Dense(43,activation='softmax')(logits)
c_model = Model(input_img, res)
c_model.summary()
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
c_model.compile(loss='categorical_crossentropy', optimizer= adam, metrics=['accuracy'])
tensor_board = TensorBoard(log_dir='./logs', histogram_freq=1)
history = c_model.fit(X_train, y_train, batch_size=128,nb_epoch=3,shuffle=True,verbose=1,validation_split=0.25,callbacks=[tensor_board])
loss_and_metrics = c_model.evaluate(X_test, y_test, batch_size=128)
KTF.set_session(old_session)
但错误发生如下：
File "/home/jasontian/enter/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile execfile(filename, namespace)
File "/home/jasontian/enter/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile exec(compile(f.read(), filename, 'exec'), namespace)
File "/media/jasontian/keras_tf.py", line 111, in history = c_model.fit(X_train, y_train, batch_size=128,nb_epoch=3,shuffle=True,verbose=1,validation_split=0.25,callbacks=[tensor_board])
File "/home/jasontian/enter/lib/python3.5/site-packages/keras/engine/training.py", line 1196, in fit initial_epoch=initial_epoch) File "/home/jasontian/enter/lib/python3.5/site-packages/keras/engine/training.py", line 911, in _fit_loop callbacks.on_epoch_end(epoch, epoch_logs)
File "/home/jasontian/enter/lib/python3.5/site-packages/keras/callbacks.py", line 76, in on_epoch_end callback.on_epoch_end(epoch, logs)
File "/home/jasontian/enter/lib/python3.5/site-packages/keras/callbacks.py", line 653, in on_epoch_end result = self.sess.run([self.merged], feed_dict=feed_dict)
File "/home/jasontian/enter/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 766, in run run_metadata_ptr)
File "/home/jasontian/enter/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 921, in _run + e.args[0]) TypeError: Cannot interpret feed_dict key as Tensor: Can not convert a int into a Tensor.
起初我以为它可能是y_train.dtype（它是float64），但我发现它在一个例子中效果很好。
update:the X_train 的形状是 (39209,32,32,1)。
那么我该如何解决呢？
没有 tf session 不行吗？如果你真的不需要会话，你可以试试这个：
from keras.layers import merge, Dropout, Convolution2D, MaxPooling2D, Input, Dense, Flatten, Merge
from keras.models import Model
from keras.callbacks import EarlyStopping, ReduceLROnPlateau,TensorBoard
import pickle
from sklearn.utils import shuffle
import numpy as np
import random
from keras.optimizers import Adam
np.random.seed(1000)
def load_pickled_data(file, columns):
with open(file, mode='rb') as f:
dataset = pickle.load(f)
return tuple(map(lambda c: dataset[c], columns))
train_preprocessed_dataset_file = "train.p"
test_preprocessed_dataset_file = "test.p"
X_train, y_train_64 = load_pickled_data(train_preprocessed_dataset_file, columns = ['features', 'labels'])
X_test, y_test_64 = load_pickled_data(test_preprocessed_dataset_file, columns = ['features', 'labels'])
y_train = y_train_64.astype(np.float32)
y_test = y_test_64.astype(np.float32)
###CNN model###
input_img = Input(shape=(32, 32, 1))
conv_1 = Convolution2D(32, 5, 5, border_mode='same', activation='relu')(input_img)
pool_1 = MaxPooling2D((2, 2))(conv_1)
pool_1 = Dropout(0.1)(pool_1)
conv_2 = Convolution2D(64, 5, 5, border_mode='same', activation='relu')(pool_1)
pool_2 = MaxPooling2D((2, 2))(conv_2)
pool_2 = Dropout(0.2)(pool_2)
conv_3 = Convolution2D(128, 5, 5, border_mode='same', activation='relu')(pool_2)
pool_3 = MaxPooling2D((2, 2))(conv_3)
pool_3 = Dropout(0.3)(pool_3)
pool_3 = Flatten()(pool_3)
pool_1 = MaxPooling2D((4, 4))(pool_1)
pool_1 = Flatten()(pool_1)
pool_2 = MaxPooling2D((2, 2))(pool_2)
pool_2 =Flatten()(pool_2)
all_features = merge([pool_1, pool_2, pool_3], mode='concat')
logits = Dense(500,activation='relu')(all_features)
logits = Dropout(0.5)(logits)
res = Dense(43,activation='softmax')(logits)
c_model = Model(input_img, res)
c_model.summary()
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
c_model.compile(loss='categorical_crossentropy', optimizer= adam, metrics=['accuracy'])
tensor_board = TensorBoard(log_dir='./logs', histogram_freq=1)
history = c_model.fit(X_train, y_train, batch_size=128,nb_epoch=3,shuffle=True,verbose=1,validation_split=0.25,callbacks=[tensor_board])
loss_and_metrics = c_model.evaluate(X_test, y_test, batch_size=128)
如果您的 keras 默认后端是 Tensorflow，则不必指定它。