在 Mac Pro 上训练 MNIST 数据通常需要多长时间？

数据挖掘 美国有线电视新闻网 极简主义
2021-10-05 21:46:14

我的代码如下：

# define a simple CNN model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Conv2D(30, (5, 5), input_shape=(1, 28, 28), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(15, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(50, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model


# build the model
model = baseline_model()
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=500, batch_size=200)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)

我的 Mac 容量：

Grphics Radeon Pro 555 2048 MB
Intel HD Graphics 630 1536 MB

Memeory 16 GB 2133 MHz LPDDR3

Processor 2.8 GHz Intel Core i7

在我的计算机上训练这个 MNIST 图像分类数据集需要几个小时。这是正常的吗？

2个回答

您是否尝试进行模型摘要并检查模型的大小。是的，我可能需要几个小时，因为你要添加很多层。尝试从 10 或 20 个 epoch 开始，逐步检查模型的准确性。可以减少你的训练时间。

当您进行上述练习时，最好有一个验证数据集，而不是一次又一次地将模型拟合到训练数据集上。

MNIST 输入形状的参数顺序错误。它应该是input_shape=(28, 28, 1)

此外，有时在代码工作之前不要在函数中编写代码是有意义的。在函数中调试代码更加困难。

我已经创建了您的代码的工作版本。它显示一个进度条，因此您应该能够看到代码估计需要多长时间才能完成。

import numpy as np
from tensorflow import keras
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense

# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)

# the data, split between train and test sets
(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()

# Scale images to the [0, 1] range
X_train = X_train.astype("float32") / 255
X_test = X_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
X_train = np.expand_dims(X_train, -1)
X_test = np.expand_dims(X_test, -1)
print("x_train shape:", X_train.shape)
print(X_train.shape[0], "train samples")
print(X_test.shape[0], "test samples")

# 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)

def baseline_model():
    "Define a simple CNN model"
    
    # create model
    model = Sequential()
    model.add(Conv2D(30, (5, 5), input_shape=(28, 28, 1), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(15, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(50, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# Build the model
model = baseline_model()

# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=500, batch_size=200)

# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
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