Access Model Training History in TFLearn? - tflearn

Question

I want to save train and validation loss in csv files in tflearn, and then reload it like we do in keras with history to plot graphs. Please help me

Answer 1

I'm not sure this will help but you can save the model with this code: model.save('mnist.tflearn') After that whenever you want, you can to reload the model back. Below is an example to reload the model:
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)