TFLearnのアクセスモデルトレーニングの歴史？

Question

tflearnのcsvファイルに列車と検証の損失を保存し、グラフをプロットする履歴を持つkerasのように再ロードします。助けてください

Answer 1

このコードでモデルを保存することはできますが、model.save('mnist.tflearn')を保存しておいてから、いつでもモデルをリロードすることができます。以下は、モデルをリロードする例です。

from __future__ import division, print_function, absolute_import 
import tflearn.datasets.mnist as mnist 
import csv 
import tflearn 
from tflearn.layers.core import input_data,dropout, fully_connected 
from tflearn.layers.conv import conv_2d, max_pool_2d 
from tflearn.layers.estimator import regression 
from tflearn.data_preprocessing import ImagePreprocessing 
from tflearn.data_augmentation import ImageAugmentation 
from tflearn.metrics import Accuracy 


X, Y, test_x, test_y = mnist.load_data(one_hot=True) 

shape = 28 
X = X.reshape([-1, shape, shape, 1]) 
test_x = test_x.reshape([-1, shape, shape, 1]) 

################################### 
# Image transformations 
################################### 

# normalisation of images 
img_prep = ImagePreprocessing() 
img_prep.add_featurewise_zero_center() 
img_prep.add_featurewise_stdnorm() 

# Create extra synthetic training data by flipping & rotating images 
img_aug = ImageAugmentation() 
img_aug.add_random_flip_leftright() 
img_aug.add_random_rotation(max_angle=25.) 

################################### 
# Define network architecture 
################################### 

# Input is a 32x32 image with 3 color channels (red, green and blue) 
network = input_data(shape=[None, shape, shape, 1], 
        data_preprocessing=img_prep, 
        data_augmentation=img_aug) 


# 1: Convolution layer with 32 filters, each 3x3x3 
conv_1 = conv_2d(network, 32, 2, activation='relu', name='conv_1') 

# 2: Max pooling layer 
network = max_pool_2d(conv_1, 2) 
# 12: Dropout layer to combat overfitting 
network = dropout(network, 0.8) 

# 3: Convolution layer with 64 filters 
conv_2 = conv_2d(network, 64, 2, activation='relu', name='conv_2') 

# 2: Max pooling layer 
network = max_pool_2d(conv_2, 2) 
# 12: Dropout layer to combat overfitting 
network = dropout(network, 0.8) 

# 4: Convolution layer with 64 filters 
conv_3 = conv_2d(network, 64, 2, activation='relu', name='conv_3') 

# 5: Max pooling layer 
network = max_pool_2d(conv_3, 2) 
# 12: Dropout layer to combat overfitting 
network = dropout(network, 0.8) 

# 5: Convolution layer with 64 filters 
conv_4 = conv_2d(network, 128, 2, activation='relu', name='conv_4') 

# 6: Max pooling layer 
network = max_pool_2d(conv_4, 2) 
# 12: Dropout layer to combat overfitting 
network = dropout(network, 0.8) 

# 7: Convolution layer with 64 filters 
conv_5 = conv_2d(network, 256, 2, activation='relu', name='conv_5') 

# 8: Max pooling layer 
network = max_pool_2d(conv_5, 2) 
# 12: Dropout layer to combat overfitting 
network = dropout(network, 0.8) 

# 9: Convolution layer with 64 filters 
conv_6 = conv_2d(network, 256, 2, activation='relu', name='conv_6') 

# 10: Max pooling layer 
network = max_pool_2d(conv_6, 2) 

# 12: Dropout layer to combat overfitting 
network = dropout(network, 0.8) 

# 11: Fully-connected 512 node layer 
network = fully_connected(network, 1024, activation='relu') 


# 13: Fully-connected layer with two outputs 
network = fully_connected(network, 10, activation='softmax') 

# Configure how the network will be trained 
acc = Accuracy(name="Accuracy") 
network = regression(network, optimizer='adam', 
        loss='categorical_crossentropy', 
        learning_rate=0.0005, metric=acc) 

# Wrap the network in a model object 
model = tflearn.DNN(network) 
model.load('mnist.tflearn') 
for i in xrange(0, len(testX)): 
    im = [testX[i]] 
    a = model.predict(im)