Python 3.x 建立神经网络[TensorFlow 2.0]模型子分类-值错误/类型错误
我正在使用TensorFlow 2.0和Python 3.7.5构建一个使用模型子分类方法的虹膜分类神经网络 我的守则如下：Python 3.x 建立神经网络[TensorFlow 2.0]模型子分类-值错误/类型错误,python-3.x,keras,neural-network,tensorflow2.0,Python 3.x,Keras,Neural Network,Tensorflow2.0,我正在使用TensorFlow 2.0和Python 3.7.5构建一个使用模型子分类方法的虹膜分类神经网络 我的守则如下： import tensorflow as tf from tensorflow.keras import Sequential, Model from tensorflow.keras.layers import Dense, Input import pandas as pd import numpy as np # Read in data- data = pd.
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense, Input
import pandas as pd
import numpy as np
# Read in data-
data = pd.read_csv("iris.csv")
# Get data types for different attributes-
data.dtypes
'''
sepallength float64
sepalwidth float64
petallength float64
petalwidth float64
class object
dtype: object
'''
# Get shape of data-
data.shape
# (150, 5)
# Check for missing values-
data.isnull().values.any()
# False
# Perform label encoding for target variable-
# Initialize a label encoder-
le = LabelEncoder()
# Label encode target attribute-
data['class'] = le.fit_transform(data['class'])
# Get different classes which are label encoded-
le.classes_
# array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)
# Split data into features (X) and target (y)-
X = data.drop('class', axis = 1)
y = data['class']
# Get training & testing sets using features and labels-
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# Convert from Pandas to numpy arrays-
X_train = X_train.to_numpy()
X_test = X_test.to_numpy()
y_train = y_train.to_numpy()
y_test = y_test.to_numpy()
print("\nTraining and Testing set dimensions:")
print("X_train.shape = {0}, y_train.shape = {1}".format(X_train.shape, y_train.shape))
print("X_test.shape = {0}, y_test.shape = {1}\n".format(X_test.shape, y_test.shape))
# Training and Testing set dimensions:
# X_train.shape = (105, 4), y_train.shape = (105,)
# X_test.shape = (45, 4), y_test.shape = (45,)
class IrisClassifier(Model):
def __init__(self):
super(IrisClassifier, self).__init__()
'''
self.layer1 = Dense(
units = 4, activation = 'relu',
kernel_initializer = tf.keras.initializers.GlorotNormal()
)
'''
self.input_layer = Input(
shape = (4,)
)
self.layer1 = Dense(
units = 10, activation = 'relu',
input_dim = 4,
kernel_initializer = tf.keras.initializers.GlorotNormal()
)
self.layer2 = Dense(
units = 10, activation = 'relu',
kernel_initializer = tf.keras.initializers.GlorotNormal()
)
self.outputlayer = Dense(
units = 3, activation = 'softmax'
)
def call(self, x):
x = self.input_layer(x)
x = self.layer1(x)
x = self.layer2(x)
# x = self.layer3(x)
return self.outputlayer(x)
# Instantiate a model of defined neural network class-
model = IrisClassifier()
# Define EarlyStopping callback-
callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
# Compile defined model-
model.compile(
optimizer=tf.keras.optimizers.Adam(lr = 0.001),
loss = 'sparse_categorical_crossentropy',
metrics = ['accuracy']
)
# Train model-
history2 = model.fit(
x = X_train, y = y_train,
validation_data = [X_test, y_test],
epochs = 50, batch_size = 16,
callbacks = [callback]
)
2.2 7 5.0 3.4 1.5 0.2 73 6.1 2.8 4.7 1.2 92 5.8 2.6 4.0 1.2 87 6.3 2.3 4.4 1.3 .. ... ... ... ... 93 5.0 2.3.3 1.0 30 4.8 3.1 1.6 0.2 25 5.0 3.0 1.6 0.2 31 5.4 3.4 1.5 0.4 97 6.2 2 2.9 4.3 1.3 [105行x 4列] 将X_-train、y_-train、X_-test和y_-test转换为numpy数组后，当我执行history2来训练模型时，我得到以下错误： TypeError:在转换的代码中： 改为“str”类型的“collections” 怎么了
谢谢 您的问题源于在将数据拟合到模型之前对数据进行预处理的方式 很可能是您从iris传递了整个csv数据集，包括列标题，从而导致了问题。您可以从中验证这一点 “您通过了：输入=separalength separalwidth petalalength petalwidth 117 7.7 3.8 6.7”
确保Xs和ys不包含列名，而只包含值。使用
X\u train=X\u train.to\u numpy（）X\u train.values谢谢 请至少投票，因为它解决了你的第一个问题。如果你有其他问题，请问另一个问题，而不是在一个问题中有一系列问题。谢谢，这不是一系列的问题。在我将培训和测试集转换为numpy（如您所建议）之后，我遇到了一个新错误，即TypeError。到目前为止，我还没有在上一期中运行过，我正在进行研究以帮助您。如果您注意的话，我会对您的其他问题给出类似的回答。很高兴你解决了这个问题DI正在寻找我在回答中引用的解释，因为这提供了清晰的解释。但是，谢谢D
<ipython-input-14-ae6111e00410>:34 call *
x = self.input_layer(x)
/home/arjun/.local/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py:427
TypeError: Expected float32 passed to parameter 'y' of op 'Equal', got 'collections' of type 'str' instead. Error: Expected float32, got
# Define EarlyStopping callback-
callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
class IrisClassifier(Model):
def __init__(self):
super(IrisClassifier, self).__init__()
self.layer1 = Dense(
units = 10, activation = 'relu',
# input_dim = 4,
kernel_initializer = tf.keras.initializers.GlorotNormal()
)
self.layer2 = Dense(
units = 10, activation = 'relu',
kernel_initializer = tf.keras.initializers.GlorotNormal()
)
self.outputlayer = Dense(
units = 3, activation = 'softmax'
)
def call(self, x):
# x = self.input_layer(x)
x = self.layer1(x)
x = self.layer2(x)
# x = self.layer3(x)
return self.outputlayer(x)
# Instantiate a model of defined neural network class-
model2 = IrisClassifier()
# Compile defined model-
model2.compile(
optimizer=tf.keras.optimizers.Adam(lr = 0.001),
loss = 'sparse_categorical_crossentropy',
metrics = ['accuracy']
)
# Train model-
history2 = model2.fit(
x = X_train, y = y_train,
validation_data = [X_test, y_test],
epochs = 50, batch_size = 16,
callbacks = [callback]
)