Kerasでcifar10のデータセットを転移学習を用いて分類するという目的のコードなのですが、エラーが出てきてこれはどういうことなのでしょうか？
ソースコード
from keras import optimizers
from keras.applications.vgg16 import VGG16
from keras.datasets import cifar10
from keras.layers import Dense, Dropout, Flatten, Input
from keras.models import Model, Sequential
from keras.utils.np_utils import to_categorical
import matplotlib.pyplot as plt
import numpy as np
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train[:300]
X_test = X_test[:100]
y_train = to_categorical(y_train)[:300]
y_test = to_categorical(y_test)[:100]
#input_tensorを定義
input_tensor = Input(shape=(32, 32, 3))
vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor)
top_model = Sequential()
top_model.add(Flatten(input_shape=vgg16.output_shape[1:]))
top_model.add(Dense(256, activation='sigmoid'))
top_model.add(Dropout(0.5))
top_model.add(Dense(10, activation='softmax'))
# vgg16とtop_modelを連結
model = Model(inputs=vgg16.input, outputs=top_model(vgg16.output))
# 19層目までの重みをfor文を用いて固定
for layer in model.layers[:19]:
layer.trainable = False
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
model.load_weights('param_vgg.hdf5')
model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=32, epochs=1)
# 以下でモデルの重みを保存する
# model.save_weights('param_vgg.hdf5')
# 精度の評価
scores = model.evaluate(X_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
# データの可視化（テストデータの先頭の10枚）
for i in range(10):
plt.subplot(2, 5, i+1)
plt.imshow(X_test[i])
plt.suptitle("テストデータの先頭の10枚",fontsize=16)
plt.show()
# 予測（テストデータの先頭の10枚）
pred = np.argmax(model.predict(X_test[0:10]), axis=1)
print(pred)
model.summary()
エラーメッセージ
----------------------------------------
Using TensorFlow backend.
WARNING:tensorflow:From /Users/ipodtao/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /Users/ipodtao/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-1-da6afae9ece9> in <module>
37
38
---> 39 model.load_weights('param_vgg.hdf5')
40
41 model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=32, epochs=1)
~/anaconda3/lib/python3.7/site-packages/keras/engine/network.py in load_weights(self, filepath, by_name, skip_mismatch, reshape)
1155 if h5py is None:
1156 raise ImportError('`load_weights` requires h5py.')
-> 1157 with h5py.File(filepath, mode='r') as f:
1158 if 'layer_names' not in f.attrs and 'model_weights' in f:
1159 f = f['model_weights']
~/anaconda3/lib/python3.7/site-packages/h5py/_hl/files.py in __init__(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, **kwds)
392 fid = make_fid(name, mode, userblock_size,
393 fapl, fcpl=make_fcpl(track_order=track_order),
--> 394 swmr=swmr)
395
396 if swmr_support:
~/anaconda3/lib/python3.7/site-packages/h5py/_hl/files.py in make_fid(name, mode, userblock_size, fapl, fcpl, swmr)
168 if swmr and swmr_support:
169 flags |= h5f.ACC_SWMR_READ
--> 170 fid = h5f.open(name, flags, fapl=fapl)
171 elif mode == 'r+':
172 fid = h5f.open(name, h5f.ACC_RDWR, fapl=fapl)
h5py/_objects.pyx in h5py._objects.with_phil.wrapper()
h5py/_objects.pyx in h5py._objects.with_phil.wrapper()
h5py/h5f.pyx in h5py.h5f.open()
OSError: Unable to open file (unable to open file: name = 'param_vgg.hdf5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)