Tensorflow 似乎使用的是系统内存而不是 GPU，并且程序在 global_variable_initializer() 之后停止

I just got a new GTX 1070 Founders Addition for my desktop, and I am trying to run tensorflow on this new GPU. I am using tensorflow.device() to run tensorflow on my GPU, but it seems like this is not happening. Instead it is using cpu, and almost all of my systems 8GB of ram. Here is my code:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import matplotlib.image as mpimg
import math

print("

")
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#
with tf.device("/gpu:0"):
    # Helper Function To Print Percentage
    def showPercent(num, den, roundAmount):
        print(  str( round((num / den) * roundAmount )/roundAmount ) + " % ", end="")
    # Defince The Number Of Images To Get
    def getFile(dir, getEveryNthLine):
        allFiles = list(os.listdir(dir))
        fileNameList = []

        numOfFiles = len(allFiles)
        i = 0
        for fichier in allFiles:
            if(i % 100 == 0):
                showPercent(i, numOfFiles, 100)

            if(i % getEveryNthLine == 0):
                if(fichier.endswith(".png")):
                    fileNameList.append(dir + "/" + fichier[0:-4])
            i += 1
        return fileNameList

    # Other Helper Functions
    def init_weights(shape):
        init_random_dist = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float16)
        return tf.Variable(init_random_dist)
    def init_bias(shape):
        init_bias_vals = tf.constant(0.1, shape=shape, dtype=tf.float16)
        return tf.Variable(init_bias_vals)
    def conv2d(x, W):
        # x --> [batch, H, W, Channels]
        # W --> [filter H, filter W, Channels IN, Channels Out]

        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
    def max_pool_2by2(x):
        # x --> [batch, H, W, Channels]
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
    def convolutional_layer(input_x, shape):
        W = init_weights(shape)
        b = init_bias([ shape[3] ])
        return tf.nn.relu(conv2d(input_x, W) + b)
    def normal_full_layer(input_layer, size):
        input_size = int(input_layer.get_shape()[1])
        W = init_weights([input_size, size])
        b = init_bias([size])
        return tf.matmul(input_layer, W) + b

    print("Getting Images")
    fileNameList = getFile("F:cartoonset10k-small", 1000)
    print("
loaded " + str(len(fileNameList)) + " files")

    print("Defining Placeholders")
    x_ph = tf.placeholder(tf.float16, shape=[None, 400, 400, 4])
    y_ph = tf.placeholder(tf.float16, shape=[None])

    print("Defining Conv and Pool layer 1")
    convo_1 = convolutional_layer(x_ph, shape=[5, 5, 4, 32])
    convo_1_pooling = max_pool_2by2(convo_1)

    print("Defining Conv and Pool layer 2")
    convo_2 = convolutional_layer(convo_1_pooling, shape=[5, 5, 32, 64])
    convo_2_pooling = max_pool_2by2(convo_2)

    print("Define Flat later and a Full layer")
    convo_2_flat = tf.reshape(convo_2_pooling, [-1, 400 * 400 * 64])
    full_layer_one = tf.nn.relu(normal_full_layer(convo_2_flat, 1024))
    y_pred = full_layer_one # Add Dropout Later

    def getLabels(filePath):
        df = []
        with open(filePath, "r") as file:
            for line in list(file):
                tempList = line.replace("
", "").replace('"', "").replace(" ", "").split(",")
                df.append({
                    "attr": tempList[0],
                    "value":int(tempList[1]),
                    "maxValue":int(tempList[2])
                })
        return df

    print("
Splitting And Formating X, and Y Data")
    x_data = []
    y_data = []
    numOfFiles = len(fileNameList)
    i = 0
    for file in fileNameList:
        if i % 10 == 0:
            showPercent(i, numOfFiles, 100)
        x_data.append(mpimg.imread(file + ".png"))
        y_data.append(pd.DataFrame(getLabels(file + ".csv"))["value"][0])
        i += 1

    print("
Conveting x_data to list")
    i = 0
    for indx in range(len(x_data)):
        if i % 10 == 0:
            showPercent(i, numOfFiles, 100)
        x_data[indx] = x_data[indx].tolist()
        i += 1

    print("

Performing Train Test Split")
    train_x, test_x, train_y, test_y = train_test_split(x_data, y_data, test_size=0.2)

    print("Defining Loss And Optimizer")
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits_v2(
            labels=y_ph,
            logits=y_pred
        )
    )
    optimizer = tf.train.AdadeltaOptimizer(learning_rate=0.001)
    train = optimizer.minimize(cross_entropy)

    print("Define Var Init")
    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        print("Checkpoint Before Initializer")
        sess.run(init)
        print("Checkpoint After Initializer")
        batch_size = 8
        steps = 1
        i = 0
        for i in range(steps):
            if i % 10:
                print(i / 100, end="")

            batch_x = []
            i = 0
            for i in np.random.randint(len(train_x), size=batch_size):
                showPercent(i, len(train_x), 100)
                train_x[i]
            batch_x = [train_x[i] for i in np.random.randint(len(train_x), size=batch_size) ]
            batch_y = [train_y[i] for i in np.random.randint(len(train_y), size=batch_size) ]
            print(sess.run(train, {
                x_ph:train_x,
                y_ph:train_y,
            }))

如果你运行它，当我运行 global_variable_initializer() 时，这个程序似乎退出了.它还在终端中打印:20971520000 的分配超过了系统内存的 10%. 在查看我的任务管理器时，我看到了这个:

import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

def create_model():
    model = tf.keras.models.Sequential([
      tf.keras.layers.Flatten(input_shape=(28, 28)),
      tf.keras.layers.Dense(512, activation=tf.nn.relu),
      tf.keras.layers.Dropout(0.2),
      tf.keras.layers.Dense(10, activation=tf.nn.softmax)
    ])
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model    

epoch = 3

print('GPU:')
with tf.device('/gpu:0'):   
    model = create_model()

    model.fit(x_train, y_train, epochs=epoch)

print('
CPU:')
with tf.device('/cpu:0'):   
    model = create_model()

    model.fit(x_train, y_train, epochs=epoch)