机器学习————花的种类识别

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accuracy =history.history['acc']

loss = history.history['loss']

val_loss = history.history['val_loss']

val_accuracy = history.history['val_acc']

plt.figure(figsize=(17, 7))

plt.subplot(2, 2, 1)

plt.plot(range(30), accuracy,'bo', label='Training Accuracy')

plt.plot(range(30), val_accuracy, label='Validation Accuracy')

plt.legend(loc='lower right')

plt.title('Accuracy : Training vs. Validation ')

plt.subplot(2, 2, 2)

plt.plot(range(30), loss,'bo' ,label='Training Loss')

plt.plot(range(30), val_loss, label='Validation Loss')

plt.title('Loss : Training vs. Validation ')

plt.legend(loc='upper right')

plt.show()

warnings.filterwarnings("ignore")

model=load_model('flower/flowermodel.h5')

img_path = "flower/test/flower1.jpg"

img = image_utils.load_img(img_path, target_size=(150,150))

img_tensor = image_utils.img_to_array(img)

img_tensor = np.expand_dims(img_tensor, axis=0)

img_tensor /= 255.

print(img_tensor.shape)

plt.imshow(img_tensor[0])

layer_outputs = [layer.output for layer in model.layers[:8]]

activation_model = models.Model(inputs=model.input, outputs=layer_outputs)

activations = activation_model.predict(img_tensor)

first_layer_activation = activations[0]

plt.matshow(first_layer_activation[0,:,:,1],  cmap="viridis")

layer_names = []

for layer in model.layers[:4]:

    layer_names.append(layer.name)

images_pre_row = 16 

for layer_name, layer_activation in zip(layer_names, activations):

    n_features = layer_activation.shape[-1] 

    size = layer_activation.shape[1] 

    n_col = n_features // images_pre_row 

    display_grid = np.zeros((size * n_col, images_pre_row * size))

    for col in range(n_col):

        for row in range(images_pre_row):

            channel_image = layer_activation[0, :, :, col * images_pre_row + row]

            channel_image -= channel_image.mean()

            channel_image /= channel_image.std()

            channel_image *= 64

            channel_image += 128

            channel_image = np.clip(channel_image, 0, 255).astype("uint8")

            display_grid[col * size:(col + 1) * size, row * size:(row + 1) * size] = channel_image

    scale = 1. / size

    plt.figure(figsize=(scale * display_grid.shape[1], scale * display_grid.shape[0]))

    plt.title(layer_name)

    plt.grid(False)

    plt.imshow(display_grid, aspect="auto", cmap="viridis")

import os

import matplotlib.pyplot as plt

from PIL import Image

import os.path

def convertjpg(jpgfile, outdir, width=150, height=150): 

    img = Image.open(jpgfile)

    try:

        new_img = img.resize((width, height), Image.BILINEAR)

        new_img.save(os.path.join(outdir, os.path.basename(jpgfile)))

    except Exception as e:

        print(e)

jpgfile1 = 'flower/test/flower1.jpg' 

jpgfile2 = 'flower/test/flower2.jpg' 

jpgfile3 = 'flower/test/flower3.jpg' 

jpgfile4 = 'flower/test/flower4.jpg' 

jpgfile5 = 'flower/test/flower5.jpg' 

convertjpg(jpgfile1, "flower/newtest/") 

convertjpg(jpgfile2, "flower/newtest/") 

convertjpg(jpgfile3, "flower/newtest/") 

convertjpg(jpgfile4, "flower/newtest/") 

convertjpg(jpgfile5, "flower/newtest/") 

img_scale = plt.imread('flower/newtest/flower2.jpg')

plt.imshow(img_scale) 

img='flower/newtest/flower1.jpg'

img_scale = plt.imread(img)

plt.imshow(img_scale)

plt.show()

img_scale = img_scale.reshape(1,150,150,3).astype('float32')

img_scale = img_scale/255       

result = model.predict(img_scale)

print(result)

dict={'0':'菊花','1':'蒲公英','2':'玫瑰','3':'向日葵','4':'郁金香'}

for i in range(5):

　　if result[0][i]>0.5:

     print( '这是：'+ dict[str(i)])

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