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refer to this page for more detail
: https://www.tensorflow.org/tfx/data_validation/get_started
..
..
Thank you.
#method 1
import tensorflow as tf
tf.test.is_built_with_cuda()
> Ture
#method 2
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
> ..#method 3
import torch
torch.cuda.is_available()
>>> True
torch.cuda.current_device()
>>> 0
torch.cuda.device(0)
>>> <torch.cuda.device at 0x7efce0b03be0>
torch.cuda.device_count()
>>> 1
torch.cuda.get_device_name(0)
>>> 'GeForce GTX 950M'
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To get all nodes in the graph: (type tensorflow.core.framework.node_def_pb2.NodeDef)
all_nodes = [n for n in tf.get_default_graph().as_graph_def().node]
To get all ops in the graph: (type tensorflow.python.framework.ops.Operation)
all_ops = tf.get_default_graph().get_operations()
To get all variables in the graph: (type tensorflow.python.ops.resource_variable_ops.ResourceVariable)
all_vars = tf.global_variables()
To get all tensors in the graph: (type tensorflow.python.framework.ops.Tensor)
all_tensors = [tensor for op in tf.get_default_graph().get_operations() for tensor in op.values()]
To get all placeholders in the graph: (type tensorflow.python.framework.ops.Tensor)
all_placeholders = [placeholder for op in tf.get_default_graph().get_operations() if op.type=='Placeholder' for placeholder in op.values()]
Tensorflow 2
To get the graph in Tensorflow 2, instead of tf.get_default_graph() you need to instantiate a tf.function first and access the graph attribute, for example:
graph = func.get_concrete_function().graph
where func is a tf.function
Tensorflow 1.15.0 or 1.14.0 may require cuda 10.0 or 10.1
And your latest version of Tensorflow 2.x use cuda 11.1 or 11.0
At this situation, you need to set proper path setting.
refer to below command
>sudo nano ~/.profile
reboot after saving
Thank you.
import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten, Input from keras.layers import Conv2D, MaxPooling2D"""Build CNN Model"""num_classes = 10 input_shape = (28, 28, 1) #mnist channels first format model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy']) model.summary() """Download MNIST Data""" from keras.datasets import mnist import numpy as np # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() #(60000, 28, 28) -> (60000, 28, 28, 1) x_train = x_train.astype('float32') / 255. x_test = x_test.astype('float32') / 255. x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data format x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data format # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) """Show some images""" import matplotlib.pyplot as plt row = 10 col = 10 n = row * col plt.figure(figsize=(4, 4)) for i in range(n): # display original #https://jakevdp.github.io/PythonDataScienceHandbook/04.08-multiple-subplots.html ax = plt.subplot(row, col, i+1) plt.imshow(x_test[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show() """set up tensorboard""" from datetime import datetime import os logdir="logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S") os.makedirs(logdir, exist_ok=True) tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir) """Train model""" from keras.callbacks import TensorBoard batch_size = 128 epochs = 1 model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=True, validation_data=(x_test, y_test), callbacks=[TensorBoard(log_dir=logdir)]) score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) """test one image data """ x_test[0].shape one_image = x_test[0].reshape(1,28,28,1) y_pred_all = model.predict(one_image) y_pred_it = model.predict_classes(one_image) print(y_pred_all, y_pred_it) plt.imshow(x_test[0].reshape(28, 28)) plt.show() """save model to drive""" model.save('my_cnn_mnist_model.h5')
>cd ./logs/scalars/20190730-105257
>tensorboard --logdir=./
"""load model from drive"""
from keras.models import load_model
new_model = load_model('my_cnn_mnist_model.h5')
"""load 1 image from drive"""
from PIL import Imageimport numpy as np
"""test prediction"""img_path = './mnist_7_450.jpg'img = Image.open(img_path) #.convert("L") img = np.resize(img, (28,28,1)) im2arr = np.array(img) im2arr = im2arr.reshape(1,28,28,1) y_pred = new_model.predict_classes(im2arr) print(y_pred)
Traceback (most recent call last):
File "C:\Users\mare\Anaconda3\envs\mare4\lib\site-packages\tensorflow\python\platform\self_check.py", line 87, in preload_check
ctypes.WinDLL(build_info.cudnn_dll_name)
File "C:\Users\mare\Anaconda3\envs\mare4\lib\ctypes\__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: [WinError 126] 지정된 모듈을 찾을 수 없습니다
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\mare\Anaconda3\envs\mare4\lib\site-packages\tensorflow\__init__.py", line 24, in <module>
from tensorflow.python import *
File "C:\Users\mare\Anaconda3\envs\mare4\lib\site-packages\tensorflow\python\__init__.py", line 49, in <module>
from tensorflow.python import pywrap_tensorflow
File "C:\Users\mare\Anaconda3\envs\mare4\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 30, in <module>
self_check.preload_check()
File "C:\Users\mare\Anaconda3\envs\mare4\lib\site-packages\tensorflow\python\platform\self_check.py", line 97, in preload_check
% (build_info.cudnn_dll_name, build_info.cudnn_version_number))
ImportError: Could not find 'cudnn64_6.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Note that installing cuDNN is a separate step from installing CUDA, and this DLL is often found in a different directory from the CUDA DLLs. You may install the necessary DLL by downloading cuDNN 6 from this URL: https://developer.nvidia.com/cudnn
conda create -n tensorflow python=3.5
pip install --ignore-installed --upgrade tensorflow-gpu