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[英]ValueError: Error when checking input: expected dense_input to have 2 dimensions, but got array with shape (1, 1, 15)
[英]ValueError: Error when checking input: expected dense_151_input to have 3 dimensions, but got array with shape (2, 2100)
我正在使用Keras API編寫可以使用學習到的.h5文件進行預測的代碼。
學習模型如下
#Libraries
import keras
from keras import backend as k
from keras.models import Sequential
from keras.layers import Activation
from keras.layers.core import Dense, Flatten, Reshape
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
import numpy as np
from random import randint
from sklearn.preprocessing import MinMaxScaler
#Create 2 numpy lists that will hold both our sample data and raw data
train_labels = []
train_samples = []
#declare array to hold training data as well as label
train_samples_temp_a = []
train_samples_temp_b = []
#Generate data
for i in range(1000):
#YOUNGER PEOPLE
random_younger_a = randint(13,64)
random_younger_b = randint(13,64)
train_samples_temp_a.append(random_younger_a)
train_samples_temp_b.append(random_younger_b)
train_labels.append(0)
#OLDER PEOPLE
random_older_a = randint(65,100)
random_older_b = randint(65,100)
train_samples_temp_a.append(random_older_a)
train_samples_temp_b.append(random_older_b)
train_labels.append(1)
for i in range(50):
#YOUNGER PEOPLE
random_younger_a = randint(13,64)
random_younger_b = randint(13,64)
train_samples_temp_a.append(random_younger_a)
train_samples_temp_b.append(random_younger_b)
train_labels.append(1)
#OLDER PEOPLE
random_older_a = randint(65,100)
random_older_b = randint(65,100)
train_samples_temp_a.append(random_older_a)
train_samples_temp_b.append(random_older_b)
train_labels.append(0)
#Array of Two Arrays
train_samples.append(train_samples_temp_a)
train_samples.append(train_samples_temp_b)
#Convert both train_label and train_sample list into a numpy array
train_samples = np.array(train_samples)
train_labels = np.array(train_labels)
#Scale down train_samples to numbers between 0 and 1
scaler = MinMaxScaler(feature_range=(0,1))
scaled_train_samples=scaler.fit_transform((train_samples))
#Sequential Model
model = Sequential([
Dense(16, input_shape=(2,2100), activation='relu'),
Flatten(),
Dense(32, activation='relu'),
Dense(2, activation='softmax')
])
#Compile Model
model.compile(Adam(lr=.0001), loss='sparse_categorical_crossentropy',
metrics= ['accuracy'])
#Train Model
model.fit(scaled_train_samples, train_labels, validation_split = 0.20,
batch_size=10, epochs=20, shuffle=True, verbose=2)
我使用了Transpose函數將scaled_train_samples從2 x 2100矩陣重塑為2100 x 2矩陣。 謝謝你們的貢獻。
#Transpose
scaled_train_samples = scaled_train_samples.transpose()
但是,運行下面的代碼行可以保證模型的准確性。 目前,我的准確度為51.52%,我可以做些什么來提高此模型的准確度嗎?
#Evaluate the model
scores = model.evaluate(scaled_train_samples, train_labels)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
input_shape=(2100,)
輸入的形狀不應包含批次大小。
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