python，TensorFlow和机器学习

Question

I'm working on TensorFlow with machine learning.

I got stuck in step1 and step2

step1:

X = X/255.0

TypeError: unsupported operand type(s) for /: 'list' and 'float'

step2:

model.add(Conv2D(64, (3,3), input_shape=X.shape[1:])

'list' object has no attribute 'shape'

Edit 25/08-2019 13:44

Step1: X=np.array(X)

Got new error in step2

Step2:

Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [None]

Edit 25/08-2019 19:26 my full code:

import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle
import numpy as np

X = pickle.load(open("x.pickle", "rb"))
y = pickle.load(open("y.pickle", "rb"))

X = np.array(X)

X = X/255.0
#x.shape=np.array([x])
#X = np.asarray(x).shape[1:]

print(X.shape)

model = Sequential()

model.add(Conv2D(64, (3,3), input_shape=X.shape[1:]))
#model.add(Conv2D(64, (3,3), input_shape=x.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(64))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit(X, y, batch_size=32, epochs=1, validation_split=0.1)

X.Shape = (24946,)

Answer 1

conv2d期望输入张量为[batch，in_height，in_width，in_channels]（4维形状），请检查X的形状。