我看过其他帖子说只是按预期添加额外的尺寸。首先，我不知道如何准确地做到这一点，但最重要的是，我想知道为什么我的尺寸会发生变化，以便我自己为未来的模型解决这个问题。
请注意，我仅限于 MLP 进行培训。这意味着只有全连接层。不允许使用卷积层或反馈（LSTM 或任何 RNN 架构）。不允许使用预训练模型，例如（resnet、densenet、...）。我可以在层之间使用其他操作，例如 Dropout、Batch Normalization 或其他类型的层输入/输出操作。我希望我必须提供我的整个代码才能获得我需要的帮助。请原谅我在代码中的所有评论，我有这些评论来提醒我一切都是做什么的。我知道我需要数据增强，但首先需要它。
# **********************************************************************************************************************
# ----------------------------------------------------------------------------------------------------------------------
# Goal: Use MLP to classify blood cells into 4 different categories:
# 1. red blood cell
# 2. ring - malaria
# 3. schizont - malaria
# 4. trophozoite - malaria
# Metric used: macro-averaged F1-score and
# Cohen's Kappa score - mean of these two scores
# **********************************************************************************************************************
# import Packages
# **********************************************************************************************************************
import os
import cv2
from keras.layers import Dense, Input
from sklearn.preprocessing import LabelEncoder
# https://machinelearningmastery.com/how-to-normalize-center-and-standardize-images-with-the-imagedatagenerator-in-keras/
import random
import numpy as np
import tensorflow as tf
from keras.callbacks import ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn.metrics import cohen_kappa_score, f1_score
import matplotlib.pyplot as plt
from keras.models import Model
from keras import optimizers
from keras.utils import to_categorical
# *************************************************************
# %% --------------------------------------- Set-Up --------------------------------------------------------------------
print('Set-up')
# np.load("x_train.npy"), np.load("y_train.npy")
# np.load("x_train.npy"), np.load("y_train.npy")
SEED = 42
os.environ['PYTHONHASHSEED'] = str(SEED)
random.seed(SEED)
np.random.seed(SEED)
tf.random.set_seed(SEED)
# **********************************************************************************************************************
# LOAD DATA
# **********************************************************************************************************************
print('Load Data')
if "train" not in os.listdir():
os.system("wget https://storage.googleapis.com/exam-deep-learning/train.zip")
os.system("unzip train.zip")
DATA_DIR = os.getcwd() + "/train/"
RESIZE_TO = 50 # 50 pixels
x, y = [], []
for path in [f for f in os.listdir(DATA_DIR) if f[-4:] == ".png"]: # for .png images in directory
x.append(cv2.resize(cv2.imread(DATA_DIR + path), (RESIZE_TO, RESIZE_TO))) # resizes image to dimensions
with open(DATA_DIR + path[:-4] + ".txt", "r") as s: # opens a .txt file of same name as image
label = s.read() # reads the file's contents
y.append(label) # appends the contents to y data set
x, y = np.array(x), np.array(y) # sets x and y to an array
le = LabelEncoder() # label encoder encodes target labels with values between 0 and n_classes-1, done to y target
le.fit(["red blood cell", "ring", "schizont", "trophozoite"]) # should have 4 values; fit label encoder
y = le.transform(y) # transforms labels to normalized encoding
print(x.shape, y.shape)
# (8607, 50, 50, 3) (8607,)
print(x)
print(y)
# **********************************************************************************************************************
# SPLIT DATA
# **********************************************************************************************************************
print('Split Data')
# This script generates the training set
# and the held out set
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=SEED, test_size=0.2, stratify=y)
np.save("x_train.npy", x_train)
np.save("y_train.npy", y_train) # saves datasets as .npy files
np.save("x_test.npy", x_test)
np.save("y_test.npy", y_test)
# summarize dataset shape
print('Train shape', x_train.shape, y_train.shape)
print('Test shape', x_test.shape, y_test.shape)
# Train shape (6885, 50, 50, 3) (6885,)
# Test shape (1722, 50, 50, 3) (1722,)
# summarize pixel values
print('Train pixels', x_train.min(), x_train.max(), x_train.mean(), x_train.std())
print('Test pixels', x_test.min(), x_test.max(), x_test.mean(), x_test.std())
# Train pixels 0 255 129.69422568869524 68.92910646179355
# Test pixels 0 255 129.9020098335269 69.49813333178977
print('Training data shape : ', x_train.shape, y_train.shape)
print('Testing data shape : ', x_test.shape, y_test.shape)
# Find the unique numbers from the train labels
classes = np.unique(y_train)
nClasses = len(classes)
print('Total number of outputs : ', nClasses)
print('Output classes : ', classes)
# %% ----------------------------------- Hyper Parameters --------------------------------------------------------------
print('Set Hyperparameters')
# a model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data
LR = 1e-3 # learning rate
N_NEURONS = 4 # number of neurons
N_EPOCHS = 50 # number of epochs - # of passes through the entire training dataset the mL algorithm has completed
BATCH_SIZE = 100 # batch size - defines the scope of our data upfront. Limits the number of samples to be shown
# to the network before a weight update can be performed.
# The same limitation is then imposed when making predictions with the fit model.
# Batch size used when fitting the model controls how many predictions you must make at a time.
# https://machinelearningmastery.com/use-different-batch-sizes-training-predicting-python-keras/
DROPOUT = 0.2 # dropout - term used for a technique which drops out some nodes of the network.
# Dropping out can be seen as temporarily deactivating or ignoring neurons of the network.
# This technique is applied in the training phase to reduce overfitting effects.
# Basic idea behind dropout is to dropout nodes so that the network can concentrate on other features.
# https://www.python-course.eu/neural_networks_with_dropout.php
# Iterations is number of batches needed to complete one epoch.
# %% -------------------------------------- Data Prep ------------------------------------------------------------------
print('Data Prep')
# load training data from the .npy files created by Numpy. loads data faster with .npy
# x_train, y_train, x_test, y_test = np.load("x_train.npy"), np.load("y_train.npy"), np.load("x_test.npy"), np.load(
# "y_test.npy")
print('x_train shape: ', x_train.shape[1:])
print('x_test shape: ', x_test.shape[1:])
print('find shape of input')
# find shape of input image then reshape it into input format for training and testing sets
nRows, nCols, nDims = x_train.shape[1:]
train_data = x_train.reshape(x_train.shape[0], nRows, nCols, nDims)
test_data = x_test.reshape(x_test.shape[0], nRows, nCols, nDims)
input_shape = (nRows, nCols, nDims)
print('train_data shape: ', train_data.shape[1:])
print('test_data shape: ', test_data.shape[1:])
# then change all datatypes into floats
train_data = train_data.astype('float32')
test_data = test_data.astype('float32')
# flatten and normalize data
print('flatten and normalize data')
train_data = train_data.reshape(len(train_data), -1)
test_data = test_data.reshape(len(test_data), -1)
train_data /= 255
test_data /= 255
train_labels_one_hot = to_categorical(y_train)
test_labels_one_hot = to_categorical(y_test)
print('Original label 0 : ', y_train[0])
print('After conversion to categorical ( one-hot ) : ', train_labels_one_hot[0])
# %%-------------------------------will want to eventually do this data augmentation--------------------------
# data augmentation later
# %% -------------------------------------- Training Prep ----------------------------------------------------------
print('Training Prep')
print('Create Model')
# this returns a tensor
inputs = Input(shape=input_shape)
output_1 = Dense(N_NEURONS, activation="relu")(inputs)
output_2 = Dense(N_NEURONS, activation="relu")(output_1)
predictions = Dense(nClasses, activation="softmax")(output_2)
# This creates a model that includes the Input layer and three Dense layers
model = Model(inputs=inputs, outputs = predictions)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Before training a model, you need to configure the learning process, which is done via the compile method.
# It receives three arguments:
# 1. optimizer
# 2. loss function - objective that the model will try to minimize
# 3. list of metrics - for classification problem, set to metrics=['accuracy']
model.compile(optimizers.Adam(lr=LR), loss="categorical_crossentropy", metrics=["accuracy"])
model.summary()
# %% -------------------------------------- Training Loop ----------------------------------------------------------
print('training Loop')
model.fit(
train_data,
train_labels_one_hot,
batch_size=BATCH_SIZE,
epochs=N_EPOCHS,
validation_data=(test_data, test_labels_one_hot),
callbacks=[ModelCheckpoint("mlp_user1.hdf5", monitor="val_loss", save_best_only=True)])
# modelcheckpoint sets check-point in the learning model during training in Python using the Keras library
# a snapshot of the state of the system is taken in case of system failure. It may be used directly, or as the
# starting point for a new run, picking up where it left off.
# The checkpoint is the weights of the model. These weights can be used to make predictions as is,
# or used as the basis for ongoing training.
# https://machinelearningmastery.com/check-point-deep-learning-models-keras/
# %% ------------------------------------------ Final test -------------------------------------------------------------
print('Final Test')
print("Final accuracy on validations set:", 100 * model.evaluate(x_test, y_test)[1], "%")
print("Cohen Kappa", cohen_kappa_score(np.argmax(model.predict(x_test), axis=1), np.argmax(y_test, axis=1)))
print("F1 score", f1_score(np.argmax(model.predict(x_test), axis=1), np.argmax(y_test, axis=1), average='macro'))