transfer_train.py

# USAGE
# python train.py --dataset dataset --model pokedex.model --labelbin lb.pickle

# set the matplotlib backend so figures can be saved in the background
import matplotlib
matplotlib.use("Agg")

# import the necessary packages
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
from keras.preprocessing.image import img_to_array
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from pyimagesearch.smallervggnet import SmallerVGGNet
import matplotlib.pyplot as plt
from imutils import paths
import numpy as np
import argparse
import random
import pickle
import cv2
import os
from keras.models import load_model
import pandas as pd
from keras.models import Sequential
from keras.layers import Activation, Dense


os.environ['KMP_DUPLICATE_LIB_OK']='True'

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
	help="path to input dataset (i.e., directory of images)")
ap.add_argument("-m", "--model", required=True,
	help="path to output model")
ap.add_argument("-l", "--labelbin", required=True,
	help="path to output label binarizer")
ap.add_argument("-p", "--plot", type=str, default="plot.png",
	help="path to output accuracy/loss plot")
args = vars(ap.parse_args())

# initialize the number of epochs to train for, initial learning rate,
# batch size, and image dimensions
EPOCHS = 200
INIT_LR = 1e-3
BS = 32
IMAGE_DIMS = (96, 96, 3)

# initialize the data and labels
data = []
labels = []

# grab the image paths
print("[INFO] loading images...")
imagePaths = sorted(list(paths.list_images(args["dataset"])))

# loop over the input images
for imagePath in imagePaths:
	# load the image, pre-process it, and store it in the data list
	image = cv2.imread(imagePath)
	image = cv2.resize(image, (IMAGE_DIMS[1], IMAGE_DIMS[0]))
	image = img_to_array(image)
	data.append(image)
 

	# extract the class label from the image path and update the
	# labels list
# 	label = imagePath.split(os.path.sep)[-1]
# 	label = [x[:-1] for x in label]       
# 	labels.append(label)

	# extract the class label from txt file
	# labels list
fish_df = pd.read_csv('fishmarket/fish_market_datasets.csv')
labels = fish_df['Binomial name']
labels = list(labels)
# print(len(labels))
    
# scale the raw pixel intensities to the range [0, 1]
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
print("[INFO] data matrix: {:.2f}MB".format(
	data.nbytes / (1024 * 1000.0)))

# binarize the labels
lb = LabelBinarizer()
labels = lb.fit_transform(labels)

x = np.asarray(data)
y = np.asarray(labels)

print(x.shape)
print(y.shape)

# partition the data into training and testing splits using 80% of
# the data for training and the remaining 20% for testing
(trainX, testX, trainY, testY) = train_test_split(data,
	labels, test_size=0.2, random_state=42)

# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=25, width_shift_range=0.1,
	height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
	horizontal_flip=True, fill_mode="nearest")

# initialize the model
print("[INFO] compiling model...")
pre_model = load_model('pokedex.model')
# model = SmallerVGGNet.build(width=IMAGE_DIMS[1], height=IMAGE_DIMS[0],
# 	depth=IMAGE_DIMS[2], classes=len(lb.classes_))
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model = Sequential()

for layer in pre_model.layers[:-1]: # this is where I changed your code
    model.add(layer)    

# Freeze the layers 
for layer in model.layers:
    layer.trainable = False

# Add 'softmax' instead of earlier 'prediction' layer.
model.add(Dense(37, activation='softmax'))

for layer in model.layers[-1:]:
    layer.name = layer.name + str("_2")

model.compile(loss="categorical_crossentropy", optimizer=opt,
	metrics=["accuracy"])

# train the network
print("[INFO] training network...")
H = model.fit_generator(
	aug.flow(trainX, trainY, batch_size=BS),
	validation_data=(testX, testY),
	steps_per_epoch=len(trainX) // BS,
	epochs=EPOCHS, verbose=1)

# save the model to disk
print("[INFO] serializing network...")
model.save(args["model"])

# save the label binarizer to disk
print("[INFO] serializing label binarizer...")
f = open(args["labelbin"], "wb")
f.write(pickle.dumps(lb))
f.close()

# plot the training loss and accuracy
plt.style.use("ggplot")
plt.figure()
N = EPOCHS
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="upper left")
plt.savefig(args["plot"])